{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/out-of-distribution-generalization/papers/ran/1","list_of":"/task/out-of-distribution-generalization","task":"Out-of-Distribution Generalization","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":2,"rows_per_page":100,"rows":[1,100],"of":119,"counts":{"archive_papers_tagged":516,"with_a_code_link":258,"where_syntology_ran_a_sample":119,"not_listed_spam_title":0,"listed":516,"listed_where_code_ran":119,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":98,"every_run_a_failure_of_syntologys_instrument":21,"listed_with_a_run_with_no_instrument_failure":98,"listed_every_run_a_failure_of_syntologys_instrument":21,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/out-of-distribution-generalization/papers/ran/1","prev":null,"next":"/task/out-of-distribution-generalization/papers/ran/2","papers":[{"url":"/paper/memoir-lifelong-model-editing-with-minimal","slug":"memoir-lifelong-model-editing-with-minimal","title":"MEMOIR: Lifelong Model Editing with Minimal Overwrite and Informed Retention for LLMs","date":"2025-06-09","arxiv_id":"2506.07899","repositories_listed":0,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/memoir-lifelong-model-editing-with-minimal#ran","syntology_url":"https://syntology.ai/paper/2506.07899","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.07899"}},"official":null}},{"url":"/paper/tropical-attention-neural-algorithmic","slug":"tropical-attention-neural-algorithmic","title":"Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms","date":"2025-05-22","arxiv_id":"2505.17190","repositories_listed":0,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tropical-attention-neural-algorithmic#ran","syntology_url":"https://syntology.ai/paper/2505.17190","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.17190"}},"official":null}},{"url":"/paper/neural-graph-pattern-machine","slug":"neural-graph-pattern-machine","title":"Beyond Message Passing: Neural Graph Pattern Machine","date":"2025-01-30","arxiv_id":"2501.18739","repositories_listed":2,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":2,"n_no_contract":4,"n_pointer_only":2,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 2 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/neural-graph-pattern-machine#ran","syntology_url":"https://syntology.ai/paper/2501.18739","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.18739"}},"official":{"repos":["zehong-wang/gpm"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/vision-transformer-neural-architecture-search","slug":"vision-transformer-neural-architecture-search","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","date":"2025-01-07","arxiv_id":"2501.03782","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/vision-transformer-neural-architecture-search#ran","syntology_url":"https://syntology.ai/paper/2501.03782","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.03782"}},"official":{"repos":["vovantuan1999a/OoD-ViT-NAS"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/lines-post-training-layer-scaling-prevents","slug":"lines-post-training-layer-scaling-prevents","title":"LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging","date":"2024-10-22","arxiv_id":"2410.17146","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/lines-post-training-layer-scaling-prevents#ran","syntology_url":"https://syntology.ai/paper/2410.17146","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.17146"}},"official":{"repos":["wang-kee/lines"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/looking-inward-language-models-can-learn","slug":"looking-inward-language-models-can-learn","title":"Looking Inward: Language Models Can Learn About Themselves by Introspection","date":"2024-10-17","arxiv_id":"2410.13787","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/looking-inward-language-models-can-learn#ran","syntology_url":"https://syntology.ai/paper/2410.13787","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.13787"}},"official":{"repos":["felixbinder/introspection_self_prediction"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/foogd-federated-collaboration-for-both-out-of","slug":"foogd-federated-collaboration-for-both-out-of","title":"FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection","date":"2024-10-15","arxiv_id":"2410.11397","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":12,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/foogd-federated-collaboration-for-both-out-of#ran","syntology_url":"https://syntology.ai/paper/2410.11397","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.11397"}},"official":{"repos":["xenialll/foogd-main"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/aha-human-assisted-out-of-distribution","slug":"aha-human-assisted-out-of-distribution","title":"AHA: Human-Assisted Out-of-Distribution Generalization and Detection","date":"2024-10-10","arxiv_id":"2410.08000","repositories_listed":0,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/aha-human-assisted-out-of-distribution#ran","syntology_url":"https://syntology.ai/paper/2410.08000","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.08000"}},"official":null}},{"url":"/paper/identifying-and-addressing-delusions-for","slug":"identifying-and-addressing-delusions-for","title":"Rejecting Hallucinated State Targets during Planning","date":"2024-10-09","arxiv_id":"2410.07096","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/identifying-and-addressing-delusions-for#ran","syntology_url":"https://syntology.ai/paper/2410.07096","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.07096"}},"official":{"repos":["mila-iqia/delusions"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/collaboration-towards-robust-neural-methods","slug":"collaboration-towards-robust-neural-methods","title":"Collaboration! Towards Robust Neural Methods for Routing Problems","date":"2024-10-07","arxiv_id":"2410.04968","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/collaboration-towards-robust-neural-methods#ran","syntology_url":"https://syntology.ai/paper/2410.04968","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.04968"}},"official":{"repos":["RoyalSkye/Routing-CNF"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/positional-attention-out-of-distribution","slug":"positional-attention-out-of-distribution","title":"Positional Attention: Expressivity and Learnability of Algorithmic Computation","date":"2024-10-02","arxiv_id":"2410.01686","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/positional-attention-out-of-distribution#ran","syntology_url":"https://syntology.ai/paper/2410.01686","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.01686"}},"official":{"repos":["opallab/positional_attention"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/out-of-distribution-generalization-via-1","slug":"out-of-distribution-generalization-via-1","title":"Out-of-distribution generalization via composition: a lens through induction heads in Transformers","date":"2024-08-18","arxiv_id":"2408.09503","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/out-of-distribution-generalization-via-1#ran","syntology_url":"https://syntology.ai/paper/2408.09503","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.09503"}},"official":{"repos":["jiajunsong629/ood-generalization-via-composition"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lca-on-the-line-benchmarking-out-of","slug":"lca-on-the-line-benchmarking-out-of","title":"LCA-on-the-Line: Benchmarking Out-of-Distribution Generalization with Class Taxonomies","date":"2024-07-22","arxiv_id":"2407.16067","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/lca-on-the-line-benchmarking-out-of#ran","syntology_url":"https://syntology.ai/paper/2407.16067","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.16067"}},"official":{"repos":["elvishelvis/lca-on-the-line"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-out-of-distribution-generalization-5","slug":"improving-out-of-distribution-generalization-5","title":"Improving Out-of-Distribution Generalization of Trajectory Prediction for Autonomous Driving via Polynomial Representations","date":"2024-07-18","arxiv_id":"2407.13431","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/improving-out-of-distribution-generalization-5#ran","syntology_url":"https://syntology.ai/paper/2407.13431","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.13431"}},"official":{"repos":["continental/everything-polynomial"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/context-guided-diffusion-for-out-of","slug":"context-guided-diffusion-for-out-of","title":"Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design","date":"2024-07-16","arxiv_id":"2407.11942","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/context-guided-diffusion-for-out-of#ran","syntology_url":"https://syntology.ai/paper/2407.11942","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.11942"}},"official":{"repos":["leojklarner/context-guided-diffusion"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/awt-transferring-vision-language-models-via","slug":"awt-transferring-vision-language-models-via","title":"AWT: Transferring Vision-Language Models via Augmentation, Weighting, and Transportation","date":"2024-07-05","arxiv_id":"2407.04603","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/awt-transferring-vision-language-models-via#ran","syntology_url":"https://syntology.ai/paper/2407.04603","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.04603"}},"official":{"repos":["MCG-NJU/AWT"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ctbench-a-library-and-benchmark-for-certified","slug":"ctbench-a-library-and-benchmark-for-certified","title":"CTBENCH: A Library and Benchmark for Certified Training","date":"2024-06-07","arxiv_id":"2406.04848","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ctbench-a-library-and-benchmark-for-certified#ran","syntology_url":"https://syntology.ai/paper/2406.04848","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.04848"}},"official":{"repos":["eth-sri/CTBench"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-divergence-fields-for-shift-robust","slug":"learning-divergence-fields-for-shift-robust","title":"Learning Divergence Fields for Shift-Robust Graph Representations","date":"2024-06-07","arxiv_id":"2406.04963","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":8,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/learning-divergence-fields-for-shift-robust#ran","syntology_url":"https://syntology.ai/paper/2406.04963","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.04963"}},"official":{"repos":["fannie1208/glind"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/feature-contamination-neural-networks-learn","slug":"feature-contamination-neural-networks-learn","title":"Feature Contamination: Neural Networks Learn Uncorrelated Features and Fail to Generalize","date":"2024-06-05","arxiv_id":"2406.03345","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/feature-contamination-neural-networks-learn#ran","syntology_url":"https://syntology.ai/paper/2406.03345","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.03345"}},"official":{"repos":["trzhang0116/feature-contamination"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-to-grok-emergence-of-in-context","slug":"learning-to-grok-emergence-of-in-context","title":"Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks","date":"2024-06-04","arxiv_id":"2406.02550","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-to-grok-emergence-of-in-context#ran","syntology_url":"https://syntology.ai/paper/2406.02550","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.02550"}},"official":{"repos":["ablghtianyi/ICL_Modular_Arithmetic"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/adapting-to-distribution-shift-by-visual","slug":"adapting-to-distribution-shift-by-visual","title":"Adapting to Distribution Shift by Visual Domain Prompt Generation","date":"2024-05-05","arxiv_id":"2405.02797","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":5,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 2 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adapting-to-distribution-shift-by-visual#ran","syntology_url":"https://syntology.ai/paper/2405.02797","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.02797"}},"official":{"repos":["guliisgreat/vdpg"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mvmoe-multi-task-vehicle-routing-solver-with","slug":"mvmoe-multi-task-vehicle-routing-solver-with","title":"MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts","date":"2024-05-02","arxiv_id":"2405.01029","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/mvmoe-multi-task-vehicle-routing-solver-with#ran","syntology_url":"https://syntology.ai/paper/2405.01029","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.01029"}},"official":{"repos":["ai4co/awesome-fm4co","royalskye/routing-mvmoe"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/in-context-symbolic-regression-leveraging","slug":"in-context-symbolic-regression-leveraging","title":"In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery","date":"2024-04-29","arxiv_id":"2404.19094","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/in-context-symbolic-regression-leveraging#ran","syntology_url":"https://syntology.ai/paper/2404.19094","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.19094"}},"official":{"repos":["merlerm/in-context-symbolic-regression"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/the-causal-chambers-real-physical-systems-as","slug":"the-causal-chambers-real-physical-systems-as","title":"The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology","date":"2024-04-17","arxiv_id":"2404.11341","repositories_listed":2,"syntology":{"n":29,"n_ran":20,"n_constructed":0,"n_ran_checked":20,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":1,"n_no_contract":19,"n_pointer_only":5,"phrase":"20 ran (of which 0 constructed an object rather than computing a result; 20 with no instrument failure: 0 honoured, 1 violated, 19 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/the-causal-chambers-real-physical-systems-as#ran","syntology_url":"https://syntology.ai/paper/2404.11341","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.11341"}},"official":{"repos":["juangamella/causal-chamber-paper"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":6,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/n-agent-ad-hoc-teamwork","slug":"n-agent-ad-hoc-teamwork","title":"N-Agent Ad Hoc Teamwork","date":"2024-04-16","arxiv_id":"2404.10740","repositories_listed":1,"syntology":{"n":18,"n_ran":13,"n_constructed":0,"n_ran_checked":7,"n_instrument":6,"n_unverified":5,"n_honours":4,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 4 honoured, 0 violated, 3 with no contract checked; 6 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/n-agent-ad-hoc-teamwork#ran","syntology_url":"https://syntology.ai/paper/2404.10740","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.10740"}},"official":{"repos":["carolinewang01/naht"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/imagination-augmented-generation-learning-to","slug":"imagination-augmented-generation-learning-to","title":"Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for Question Answering","date":"2024-03-22","arxiv_id":"2403.15268","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":5,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/imagination-augmented-generation-learning-to#ran","syntology_url":"https://syntology.ai/paper/2403.15268","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.15268"}},"official":{"repos":["xnhyacinth/iag"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/unleashing-the-power-of-meta-tuning-for-few","slug":"unleashing-the-power-of-meta-tuning-for-few","title":"Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts","date":"2024-03-13","arxiv_id":"2403.08477","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unleashing-the-power-of-meta-tuning-for-few#ran","syntology_url":"https://syntology.ai/paper/2403.08477","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.08477"}},"official":{"repos":["szc12153/sparse_meta_tuning"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-out-of-distribution-generalization-via","slug":"graph-out-of-distribution-generalization-via","title":"Graph Out-of-Distribution Generalization via Causal Intervention","date":"2024-02-18","arxiv_id":"2402.11494","repositories_listed":1,"syntology":{"n":8,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":8,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/graph-out-of-distribution-generalization-via#ran","syntology_url":"https://syntology.ai/paper/2402.11494","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11494"}},"official":{"repos":["fannie1208/canet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/hypo-hyperspherical-out-of-distribution","slug":"hypo-hyperspherical-out-of-distribution","title":"HYPO: Hyperspherical Out-of-Distribution Generalization","date":"2024-02-12","arxiv_id":"2402.07785","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/hypo-hyperspherical-out-of-distribution#ran","syntology_url":"https://syntology.ai/paper/2402.07785","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.07785"}},"official":{"repos":["deeplearning-wisc/hypo"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/on-provable-length-and-compositional","slug":"on-provable-length-and-compositional","title":"On Provable Length and Compositional Generalization","date":"2024-02-07","arxiv_id":"2402.04875","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/on-provable-length-and-compositional#ran","syntology_url":"https://syntology.ai/paper/2402.04875","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.04875"}},"official":{"repos":["facebookresearch/length-and-compositional-generalization"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-hard-to-beat-baseline-for-training-free","slug":"a-hard-to-beat-baseline-for-training-free","title":"A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation","date":"2024-02-06","arxiv_id":"2402.04087","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/a-hard-to-beat-baseline-for-training-free#ran","syntology_url":"https://syntology.ai/paper/2402.04087","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.04087"}},"official":{"repos":["mrflogs/iclr24"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-invariant-learning-with-subgraph-co","slug":"graph-invariant-learning-with-subgraph-co","title":"Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization","date":"2023-12-18","arxiv_id":"2312.10988","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":9,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/graph-invariant-learning-with-subgraph-co#ran","syntology_url":"https://syntology.ai/paper/2312.10988","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.10988"}},"official":{"repos":["bupt-gamma/igm"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/environment-aware-dynamic-graph-learning-for-1","slug":"environment-aware-dynamic-graph-learning-for-1","title":"Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization","date":"2023-11-18","arxiv_id":"2311.11114","repositories_listed":1,"syntology":{"n":37,"n_ran":28,"n_constructed":5,"n_ran_checked":25,"n_instrument":3,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":25,"n_pointer_only":25,"phrase":"28 ran (of which 5 constructed an object rather than computing a result; 25 with no instrument failure: 0 honoured, 0 violated, 25 with no contract checked; 3 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/environment-aware-dynamic-graph-learning-for-1#ran","syntology_url":"https://syntology.ai/paper/2311.11114","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.11114"}},"official":{"repos":["ringbdstack/eagle"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/active-instruction-tuning-improving-cross","slug":"active-instruction-tuning-improving-cross","title":"Active Instruction Tuning: Improving Cross-Task Generalization by Training on Prompt Sensitive Tasks","date":"2023-11-01","arxiv_id":"2311.00288","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":13,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/active-instruction-tuning-improving-cross#ran","syntology_url":"https://syntology.ai/paper/2311.00288","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.00288"}},"official":{"repos":["pluslabnlp/active-it"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/does-invariant-graph-learning-via-environment-1","slug":"does-invariant-graph-learning-via-environment-1","title":"Does Invariant Graph Learning via Environment Augmentation Learn Invariance?","date":"2023-10-29","arxiv_id":"2310.19035","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/does-invariant-graph-learning-via-environment-1#ran","syntology_url":"https://syntology.ai/paper/2310.19035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.19035"}},"official":null}},{"url":"/paper/real-fake-effective-training-data-synthesis","slug":"real-fake-effective-training-data-synthesis","title":"Real-Fake: Effective Training Data Synthesis Through Distribution Matching","date":"2023-10-16","arxiv_id":"2310.10402","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/real-fake-effective-training-data-synthesis#ran","syntology_url":"https://syntology.ai/paper/2310.10402","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.10402"}},"official":{"repos":["BAAI-DCAI/Training-Data-Synthesis"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/does-clip-s-generalization-performance-mainly","slug":"does-clip-s-generalization-performance-mainly","title":"Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?","date":"2023-10-14","arxiv_id":"2310.09562","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/does-clip-s-generalization-performance-mainly#ran","syntology_url":"https://syntology.ai/paper/2310.09562","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09562"}},"official":{"repos":["brendel-group/clip-ood"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/mcu-a-task-centric-framework-for-open-ended","slug":"mcu-a-task-centric-framework-for-open-ended","title":"Towards Evaluating Generalist Agents: An Automated Benchmark in Open World","date":"2023-10-12","arxiv_id":"2310.08367","repositories_listed":1,"syntology":{"n":19,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":13,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":19,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 13 unverified","sample_list":"/paper/mcu-a-task-centric-framework-for-open-ended#ran","syntology_url":"https://syntology.ai/paper/2310.08367","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.08367"}},"official":{"repos":["craftjarvis/mcu"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":13,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-stability-of-expressive-positional","slug":"on-the-stability-of-expressive-positional","title":"On the Stability of Expressive Positional Encodings for Graphs","date":"2023-10-04","arxiv_id":"2310.02579","repositories_listed":2,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/on-the-stability-of-expressive-positional#ran","syntology_url":"https://syntology.ai/paper/2310.02579","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.02579"}},"official":{"repos":["Graph-COM/SPE"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["found_in_text","listed"]}}},{"url":"/paper/xval-a-continuous-number-encoding-for-large","slug":"xval-a-continuous-number-encoding-for-large","title":"xVal: A Continuous Numerical Tokenization for Scientific Language Models","date":"2023-10-04","arxiv_id":"2310.02989","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/xval-a-continuous-number-encoding-for-large#ran","syntology_url":"https://syntology.ai/paper/2310.02989","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.02989"}},"official":{"repos":["PolymathicAI/xVal"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/discovering-environments-with-xrm","slug":"discovering-environments-with-xrm","title":"Discovering environments with XRM","date":"2023-09-28","arxiv_id":"2309.16748","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":2,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/discovering-environments-with-xrm#ran","syntology_url":"https://syntology.ai/paper/2309.16748","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.16748"}},"official":{"repos":["facebookresearch/XRM"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unified-model-for-image-video-audio-and","slug":"unified-model-for-image-video-audio-and","title":"UnIVAL: Unified Model for Image, Video, Audio and Language Tasks","date":"2023-07-30","arxiv_id":"2307.16184","repositories_listed":1,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":9,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/unified-model-for-image-video-audio-and#ran","syntology_url":"https://syntology.ai/paper/2307.16184","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.16184"}},"official":{"repos":["mshukor/unival"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/promptstyler-prompt-driven-style-generation","slug":"promptstyler-prompt-driven-style-generation","title":"PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization","date":"2023-07-27","arxiv_id":"2307.15199","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/promptstyler-prompt-driven-style-generation#ran","syntology_url":"https://syntology.ai/paper/2307.15199","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.15199"}},"official":null}},{"url":"/paper/topology-aware-robust-optimization-for-out-of","slug":"topology-aware-robust-optimization-for-out-of","title":"Topology-aware Robust Optimization for Out-of-distribution Generalization","date":"2023-07-26","arxiv_id":"2307.13943","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/topology-aware-robust-optimization-for-out-of#ran","syntology_url":"https://syntology.ai/paper/2307.13943","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.13943"}},"official":{"repos":["joffery/tro"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/an-empirical-investigation-of-pre-trained-1","slug":"an-empirical-investigation-of-pre-trained-1","title":"An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration","date":"2023-07-17","arxiv_id":"2307.08187","repositories_listed":1,"syntology":{"n":15,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/an-empirical-investigation-of-pre-trained-1#ran","syntology_url":"https://syntology.ai/paper/2307.08187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.08187"}},"official":{"repos":["hiroki11x/timm_ood_calibration"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/artificial-intelligence-for-science-in","slug":"artificial-intelligence-for-science-in","title":"Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems","date":"2023-07-17","arxiv_id":"2307.08423","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/artificial-intelligence-for-science-in#ran","syntology_url":"https://syntology.ai/paper/2307.08423","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.08423"}},"official":{"repos":["divelab/AIRS"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/rl-vigen-a-reinforcement-learning-benchmark-1","slug":"rl-vigen-a-reinforcement-learning-benchmark-1","title":"RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization","date":"2023-07-15","arxiv_id":"2307.10224","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rl-vigen-a-reinforcement-learning-benchmark-1#ran","syntology_url":"https://syntology.ai/paper/2307.10224","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.10224"}},"official":{"repos":["gemcollector/rl-vigen"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/distilling-large-vision-language-model-with","slug":"distilling-large-vision-language-model-with","title":"Distilling Large Vision-Language Model with Out-of-Distribution Generalizability","date":"2023-07-06","arxiv_id":"2307.03135","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/distilling-large-vision-language-model-with#ran","syntology_url":"https://syntology.ai/paper/2307.03135","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.03135"}},"official":{"repos":["xuanlinli17/large_vlm_distillation_ood"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/individual-and-structural-graph-information","slug":"individual-and-structural-graph-information","title":"Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization","date":"2023-06-28","arxiv_id":"2306.15902","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/individual-and-structural-graph-information#ran","syntology_url":"https://syntology.ai/paper/2306.15902","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.15902"}},"official":{"repos":["yangling0818/graphood"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/simple-and-fast-group-robustness-by-automatic","slug":"simple-and-fast-group-robustness-by-automatic","title":"Simple and Fast Group Robustness by Automatic Feature Reweighting","date":"2023-06-19","arxiv_id":"2306.11074","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/simple-and-fast-group-robustness-by-automatic#ran","syntology_url":"https://syntology.ai/paper/2306.11074","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.11074"}},"official":{"repos":["andpotap/afr"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/understanding-and-improving-feature-learning-1","slug":"understanding-and-improving-feature-learning-1","title":"Understanding and Improving Feature Learning for Out-of-Distribution Generalization","date":"2023-04-22","arxiv_id":"2304.11327","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/understanding-and-improving-feature-learning-1#ran","syntology_url":"https://syntology.ai/paper/2304.11327","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.11327"}},"official":null}},{"url":"/paper/generalization-on-the-unseen-logic-reasoning","slug":"generalization-on-the-unseen-logic-reasoning","title":"Generalization on the Unseen, Logic Reasoning and Degree Curriculum","date":"2023-01-30","arxiv_id":"2301.13105","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/generalization-on-the-unseen-logic-reasoning#ran","syntology_url":"https://syntology.ai/paper/2301.13105","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.13105"}},"official":{"repos":["aryol/gotu"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/empirical-study-on-optimizer-selection-for","slug":"empirical-study-on-optimizer-selection-for","title":"Empirical Study on Optimizer Selection for Out-of-Distribution Generalization","date":"2022-11-15","arxiv_id":"2211.08583","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/empirical-study-on-optimizer-selection-for#ran","syntology_url":"https://syntology.ai/paper/2211.08583","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.08583"}},"official":{"repos":["hiroki11x/optimizer_comparison_ood"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/adversarial-causal-augmentation-for-graph","slug":"adversarial-causal-augmentation-for-graph","title":"Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift","date":"2022-11-05","arxiv_id":"2211.02843","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adversarial-causal-augmentation-for-graph#ran","syntology_url":"https://syntology.ai/paper/2211.02843","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.02843"}},"official":{"repos":["yongduosui/aia"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-better-out-of-distribution","slug":"towards-better-out-of-distribution","title":"Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks","date":"2022-11-01","arxiv_id":"2211.00692","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/towards-better-out-of-distribution#ran","syntology_url":"https://syntology.ai/paper/2211.00692","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.00692"}},"official":{"repos":["smahdavi4/clrs"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/broken-neural-scaling-laws","slug":"broken-neural-scaling-laws","title":"Broken Neural Scaling Laws","date":"2022-10-26","arxiv_id":"2210.14891","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":4,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/broken-neural-scaling-laws#ran","syntology_url":"https://syntology.ai/paper/2210.14891","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.14891"}},"official":{"repos":["ethancaballero/broken_neural_scaling_laws"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/hypothesis-testing-using-causal-and-causal","slug":"hypothesis-testing-using-causal-and-causal","title":"Causal Structural Hypothesis Testing and Data Generation Models","date":"2022-10-20","arxiv_id":"2210.11275","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":4,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hypothesis-testing-using-causal-and-causal#ran","syntology_url":"https://syntology.ai/paper/2210.11275","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11275"}},"official":{"repos":["sunaybhat1/causal-structural-hypothesis-testing"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-policy-guided-imitation-approach-for","slug":"a-policy-guided-imitation-approach-for","title":"A Policy-Guided Imitation Approach for Offline Reinforcement Learning","date":"2022-10-15","arxiv_id":"2210.08323","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/a-policy-guided-imitation-approach-for#ran","syntology_url":"https://syntology.ai/paper/2210.08323","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.08323"}},"official":{"repos":["ryanxhr/por"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-out-of-distribution-generalization-1","slug":"improving-out-of-distribution-generalization-1","title":"Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors","date":"2022-10-13","arxiv_id":"2210.06807","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/improving-out-of-distribution-generalization-1#ran","syntology_url":"https://syntology.ai/paper/2210.06807","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06807"}},"official":{"repos":["novaglow646/nips22-mat-and-ldat-for-ood"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/class-is-invariant-to-context-and-vice-versa","slug":"class-is-invariant-to-context-and-vice-versa","title":"Class Is Invariant to Context and Vice Versa: On Learning Invariance for Out-Of-Distribution Generalization","date":"2022-08-06","arxiv_id":"2208.03462","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/class-is-invariant-to-context-and-vice-versa#ran","syntology_url":"https://syntology.ai/paper/2208.03462","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.03462"}},"official":{"repos":["simpleshinobu/irmcon"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/discover-and-mitigate-unknown-biases-with","slug":"discover-and-mitigate-unknown-biases-with","title":"Discover and Mitigate Unknown Biases with Debiasing Alternate Networks","date":"2022-07-20","arxiv_id":"2207.10077","repositories_listed":1,"syntology":{"n":9,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":9,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/discover-and-mitigate-unknown-biases-with#ran","syntology_url":"https://syntology.ai/paper/2207.10077","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10077"}},"official":{"repos":["zhihengli-UR/DebiAN"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/assaying-out-of-distribution-generalization","slug":"assaying-out-of-distribution-generalization","title":"Assaying Out-Of-Distribution Generalization in Transfer Learning","date":"2022-07-19","arxiv_id":"2207.09239","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/assaying-out-of-distribution-generalization#ran","syntology_url":"https://syntology.ai/paper/2207.09239","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.09239"}},"official":{"repos":["amazon-research/assaying-ood"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/aug-nerf-training-stronger-neural-radiance-1","slug":"aug-nerf-training-stronger-neural-radiance-1","title":"Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations","date":"2022-07-04","arxiv_id":"2207.01164","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":5,"n_ran_checked":5,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"8 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/aug-nerf-training-stronger-neural-radiance-1#ran","syntology_url":"https://syntology.ai/paper/2207.01164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.01164"}},"official":{"repos":["vita-group/aug-nerf"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pareto-invariant-risk-minimization","slug":"pareto-invariant-risk-minimization","title":"Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization","date":"2022-06-15","arxiv_id":"2206.07766","repositories_listed":4,"syntology":{"n":10,"n_ran":6,"n_constructed":2,"n_ran_checked":4,"n_instrument":2,"n_unverified":4,"n_honours":1,"n_violates":1,"n_no_contract":2,"n_pointer_only":1,"phrase":"6 ran (of which 2 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 1 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/pareto-invariant-risk-minimization#ran","syntology_url":"https://syntology.ai/paper/2206.07766","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.07766"}},"official":{"repos":["lfhase/pair"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":2,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/transformers-are-meta-reinforcement-learners-1","slug":"transformers-are-meta-reinforcement-learners-1","title":"Transformers are Meta-Reinforcement Learners","date":"2022-06-14","arxiv_id":"2206.06614","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/transformers-are-meta-reinforcement-learners-1#ran","syntology_url":"https://syntology.ai/paper/2206.06614","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.06614"}},"official":{"repos":["luckeciano/transformers-metarl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-invariant-visual-representations-for","slug":"learning-invariant-visual-representations-for","title":"Learning Invariant Visual Representations for Compositional Zero-Shot Learning","date":"2022-06-01","arxiv_id":"2206.00415","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/learning-invariant-visual-representations-for#ran","syntology_url":"https://syntology.ai/paper/2206.00415","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.00415"}},"official":{"repos":["pris-cv/ivr"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/your-contrastive-learning-is-secretly-doing","slug":"your-contrastive-learning-is-secretly-doing","title":"Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding","date":"2022-05-30","arxiv_id":"2205.14814","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/your-contrastive-learning-is-secretly-doing#ran","syntology_url":"https://syntology.ai/paper/2205.14814","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14814"}},"official":{"repos":["Capricious-Liu/t-MoCo-v2"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/the-developmental-trajectory-of-object","slug":"the-developmental-trajectory-of-object","title":"The developmental trajectory of object recognition robustness: children are like small adults but unlike big deep neural networks","date":"2022-05-20","arxiv_id":"2205.10144","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-developmental-trajectory-of-object#ran","syntology_url":"https://syntology.ai/paper/2205.10144","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.10144"}},"official":{"repos":["wichmann-lab/robustness-development"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/diverse-weight-averaging-for-out-of","slug":"diverse-weight-averaging-for-out-of","title":"Diverse Weight Averaging for Out-of-Distribution Generalization","date":"2022-05-19","arxiv_id":"2205.09739","repositories_listed":2,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/diverse-weight-averaging-for-out-of#ran","syntology_url":"https://syntology.ai/paper/2205.09739","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.09739"}},"official":{"repos":["alexrame/diwa"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/seqzero-few-shot-compositional-semantic-1","slug":"seqzero-few-shot-compositional-semantic-1","title":"SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models","date":"2022-05-15","arxiv_id":"2205.07381","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/seqzero-few-shot-compositional-semantic-1#ran","syntology_url":"https://syntology.ai/paper/2205.07381","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.07381"}},"official":{"repos":["amzn/seqzero"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/knowledge-graph-question-answering-datasets","slug":"knowledge-graph-question-answering-datasets","title":"Knowledge Graph Question Answering Datasets and Their Generalizability: Are They Enough for Future Research?","date":"2022-05-13","arxiv_id":"2205.06573","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/knowledge-graph-question-answering-datasets#ran","syntology_url":"https://syntology.ai/paper/2205.06573","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.06573"}},"official":{"repos":["KGQA/KGQA-datasets-generalization"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/causal-transportability-for-visual","slug":"causal-transportability-for-visual","title":"Causal Transportability for Visual Recognition","date":"2022-04-26","arxiv_id":"2204.12363","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/causal-transportability-for-visual#ran","syntology_url":"https://syntology.ai/paper/2204.12363","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.12363"}},"official":{"repos":["cvlab-columbia/ct4recognition"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/what-makes-instruction-learning-hard-an","slug":"what-makes-instruction-learning-hard-an","title":"What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment","date":"2022-04-19","arxiv_id":"2204.09148","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/what-makes-instruction-learning-hard-an#ran","syntology_url":"https://syntology.ai/paper/2204.09148","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.09148"}},"official":{"repos":["allenai/regset"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/last-layer-re-training-is-sufficient-for","slug":"last-layer-re-training-is-sufficient-for","title":"Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations","date":"2022-04-06","arxiv_id":"2204.02937","repositories_listed":4,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/last-layer-re-training-is-sufficient-for#ran","syntology_url":"https://syntology.ai/paper/2204.02937","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.02937"}},"official":{"repos":["polinakirichenko/deep_feature_reweighting"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/woods-benchmarks-for-out-of-distribution","slug":"woods-benchmarks-for-out-of-distribution","title":"WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series","date":"2022-03-18","arxiv_id":"2203.09978","repositories_listed":1,"syntology":{"n":12,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/woods-benchmarks-for-out-of-distribution#ran","syntology_url":"https://syntology.ai/paper/2203.09978","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09978"}},"official":{"repos":["jc-audet/WOODS"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/model-soups-averaging-weights-of-multiple","slug":"model-soups-averaging-weights-of-multiple","title":"Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time","date":"2022-03-10","arxiv_id":"2203.05482","repositories_listed":6,"syntology":{"n":17,"n_ran":10,"n_constructed":0,"n_ran_checked":5,"n_instrument":5,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 5 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/model-soups-averaging-weights-of-multiple#ran","syntology_url":"https://syntology.ai/paper/2203.05482","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.05482"}},"official":{"repos":["mlfoundations/model-soups"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/towards-learning-causal-representations-from","slug":"towards-learning-causal-representations-from","title":"Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization","date":"2022-02-25","arxiv_id":"2202.12570","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":4,"n_ran_checked":4,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":9,"phrase":"8 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-learning-causal-representations-from#ran","syntology_url":"https://syntology.ai/paper/2202.12570","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.12570"}},"official":{"repos":["weijiazhang24/causalmil"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/invariance-principle-meets-out-of","slug":"invariance-principle-meets-out-of","title":"Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs","date":"2022-02-11","arxiv_id":"2202.05441","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/invariance-principle-meets-out-of#ran","syntology_url":"https://syntology.ai/paper/2202.05441","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.05441"}},"official":{"repos":["lfhase/ciga"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/uncertainty-modeling-for-out-of-distribution-1","slug":"uncertainty-modeling-for-out-of-distribution-1","title":"Uncertainty Modeling for Out-of-Distribution Generalization","date":"2022-02-08","arxiv_id":"2202.03958","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/uncertainty-modeling-for-out-of-distribution-1#ran","syntology_url":"https://syntology.ai/paper/2202.03958","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.03958"}},"official":{"repos":["lixiaotong97/dsu"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-domain-balanced-sampling-improves-out","slug":"multi-domain-balanced-sampling-improves-out","title":"Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models","date":"2021-12-27","arxiv_id":"2112.13734","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/multi-domain-balanced-sampling-improves-out#ran","syntology_url":"https://syntology.ai/paper/2112.13734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.13734"}},"official":{"repos":["etetteh/OoD_Gen-Chest_Xray"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-robust-and-adaptive-motion","slug":"towards-robust-and-adaptive-motion","title":"Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective","date":"2021-11-29","arxiv_id":"2111.14820","repositories_listed":2,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/towards-robust-and-adaptive-motion#ran","syntology_url":"https://syntology.ai/paper/2111.14820","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.14820"}},"official":{"repos":["sherwinbahmani/ynet_adaptive","vita-epfl/causalmotion"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/masked-autoencoders-are-scalable-vision","slug":"masked-autoencoders-are-scalable-vision","title":"Masked Autoencoders Are Scalable Vision Learners","date":"2021-11-11","arxiv_id":"2111.06377","repositories_listed":58,"syntology":{"n":137,"n_ran":86,"n_constructed":40,"n_ran_checked":69,"n_instrument":17,"n_unverified":51,"n_honours":7,"n_violates":0,"n_no_contract":62,"n_pointer_only":78,"phrase":"86 ran (of which 40 constructed an object rather than computing a result; 69 with no instrument failure: 7 honoured, 0 violated, 62 with no contract checked; 17 where Syntology's instrument failed) · 51 unverified","sample_list":"/paper/masked-autoencoders-are-scalable-vision#ran","syntology_url":"https://syntology.ai/paper/2111.06377","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.06377"}},"official":{"repos":["facebookresearch/mae"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"url":"/paper/investigating-the-effect-of-natural-language","slug":"investigating-the-effect-of-natural-language","title":"Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI","date":"2021-10-12","arxiv_id":"2110.06223","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/investigating-the-effect-of-natural-language#ran","syntology_url":"https://syntology.ai/paper/2110.06223","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.06223"}},"official":{"repos":["chicagohai/hans-explanations"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/the-connection-between-out-of-distribution","slug":"the-connection-between-out-of-distribution","title":"The Connection between Out-of-Distribution Generalization and Privacy of ML Models","date":"2021-10-07","arxiv_id":"2110.03369","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/the-connection-between-out-of-distribution#ran","syntology_url":"https://syntology.ai/paper/2110.03369","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.03369"}},"official":{"repos":["microsoft/robustdg"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fishr-invariant-gradient-variances-for-out-of","slug":"fishr-invariant-gradient-variances-for-out-of","title":"Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization","date":"2021-09-07","arxiv_id":"2109.02934","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fishr-invariant-gradient-variances-for-out-of#ran","syntology_url":"https://syntology.ai/paper/2109.02934","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.02934"}},"official":{"repos":["alexrame/fishr","facebookresearch/DomainBed"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/nas-ood-neural-architecture-search-for-out-of","slug":"nas-ood-neural-architecture-search-for-out-of","title":"NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization","date":"2021-09-05","arxiv_id":"2109.02038","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/nas-ood-neural-architecture-search-for-out-of#ran","syntology_url":"https://syntology.ai/paper/2109.02038","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.02038"}},"official":null}},{"url":"/paper/towards-robust-vision-by-multi-task-learning","slug":"towards-robust-vision-by-multi-task-learning","title":"Towards robust vision by multi-task learning on monkey visual cortex","date":"2021-07-29","arxiv_id":"2107.14344","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-robust-vision-by-multi-task-learning#ran","syntology_url":"https://syntology.ai/paper/2107.14344","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.14344"}},"official":{"repos":["sinzlab/neural_cotraining"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/delving-deep-into-the-generalization-of","slug":"delving-deep-into-the-generalization-of","title":"Delving Deep into the Generalization of Vision Transformers under Distribution Shifts","date":"2021-06-14","arxiv_id":"2106.07617","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/delving-deep-into-the-generalization-of#ran","syntology_url":"https://syntology.ai/paper/2106.07617","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.07617"}},"official":{"repos":["Phoenix1153/ViT_OOD_generalization"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/invariance-principle-meets-information","slug":"invariance-principle-meets-information","title":"Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization","date":"2021-06-11","arxiv_id":"2106.06607","repositories_listed":2,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/invariance-principle-meets-information#ran","syntology_url":"https://syntology.ai/paper/2106.06607","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06607"}},"official":{"repos":["facebookresearch/DomainBed","ahujak/IB-IRM"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/quantifying-and-improving-transferability-in","slug":"quantifying-and-improving-transferability-in","title":"Quantifying and Improving Transferability in Domain Generalization","date":"2021-06-07","arxiv_id":"2106.03632","repositories_listed":2,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/quantifying-and-improving-transferability-in#ran","syntology_url":"https://syntology.ai/paper/2106.03632","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03632"}},"official":{"repos":["facebookresearch/DomainBed","gordon-guojun-zhang/transferability-neurips2021"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/ood-bench-benchmarking-and-understanding-out","slug":"ood-bench-benchmarking-and-understanding-out","title":"OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization","date":"2021-06-07","arxiv_id":"2106.03721","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ood-bench-benchmarking-and-understanding-out#ran","syntology_url":"https://syntology.ai/paper/2106.03721","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03721"}},"official":{"repos":["ynysjtu/ood_bench"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/a-consciousness-inspired-planning-agent-for","slug":"a-consciousness-inspired-planning-agent-for","title":"A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning","date":"2021-06-03","arxiv_id":"2106.02097","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":3,"n_ran_checked":3,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/a-consciousness-inspired-planning-agent-for#ran","syntology_url":"https://syntology.ai/paper/2106.02097","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.02097"}},"official":{"repos":["mila-iqia/conscious-planning"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/towards-robust-classification-model-by","slug":"towards-robust-classification-model-by","title":"Towards Robust Classification Model by Counterfactual and Invariant Data Generation","date":"2021-06-02","arxiv_id":"2106.01127","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-robust-classification-model-by#ran","syntology_url":"https://syntology.ai/paper/2106.01127","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.01127"}},"official":{"repos":["zzzace2000/robust_cls_model"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/objective-robustness-in-deep-reinforcement","slug":"objective-robustness-in-deep-reinforcement","title":"Goal Misgeneralization in Deep Reinforcement Learning","date":"2021-05-28","arxiv_id":"2105.14111","repositories_listed":4,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":11,"n_pointer_only":2,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/objective-robustness-in-deep-reinforcement#ran","syntology_url":"https://syntology.ai/paper/2105.14111","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.14111"}},"official":{"repos":["JacobPfau/procgenAISC","jbkjr/train-procgen-pytorch"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/deep-stable-learning-for-out-of-distribution","slug":"deep-stable-learning-for-out-of-distribution","title":"Deep Stable Learning for Out-Of-Distribution Generalization","date":"2021-04-16","arxiv_id":"2104.07876","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deep-stable-learning-for-out-of-distribution#ran","syntology_url":"https://syntology.ai/paper/2104.07876","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.07876"}},"official":{"repos":["xxgege/StableNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/robust-object-detection-via-instance-level","slug":"robust-object-detection-via-instance-level","title":"Robust Object Detection via Instance-Level Temporal Cycle Confusion","date":"2021-04-16","arxiv_id":"2104.08381","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/robust-object-detection-via-instance-level#ran","syntology_url":"https://syntology.ai/paper/2104.08381","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.08381"}},"official":{"repos":["xinw1012/cycle-confusion"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/an-empirical-study-of-training-self","slug":"an-empirical-study-of-training-self","title":"An Empirical Study of Training Self-Supervised Vision Transformers","date":"2021-04-05","arxiv_id":"2104.02057","repositories_listed":9,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/an-empirical-study-of-training-self#ran","syntology_url":"https://syntology.ai/paper/2104.02057","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.02057"}},"official":{"repos":["facebookresearch/moco-v3"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"url":"/paper/learning-transferable-visual-models-from","slug":"learning-transferable-visual-models-from","title":"Learning Transferable Visual Models From Natural Language Supervision","date":"2021-02-26","arxiv_id":"2103.00020","repositories_listed":82,"syntology":{"n":20,"n_ran":16,"n_constructed":0,"n_ran_checked":2,"n_instrument":14,"n_unverified":4,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":16,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 14 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/learning-transferable-visual-models-from#ran","syntology_url":"https://syntology.ai/paper/2103.00020","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.00020"}},"official":{"repos":["openai/CLIP"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"url":"/paper/gradient-starvation-a-learning-proclivity-in","slug":"gradient-starvation-a-learning-proclivity-in","title":"Gradient Starvation: A Learning Proclivity in Neural Networks","date":"2020-11-18","arxiv_id":"2011.09468","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/gradient-starvation-a-learning-proclivity-in#ran","syntology_url":"https://syntology.ai/paper/2011.09468","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.09468"}},"official":{"repos":["mohammadpz/Gradient_Starvation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/weakly-supervised-amodal-instance-1","slug":"weakly-supervised-amodal-instance-1","title":"Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model","date":"2020-10-25","arxiv_id":"2010.13175","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/weakly-supervised-amodal-instance-1#ran","syntology_url":"https://syntology.ai/paper/2010.13175","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.13175"}},"official":{"repos":["yihongsun/bayesian-amodal"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}}],"record_sha256":"9a0ba6a932ea366613e849411f343a3bcb2d81c8aa292b97a2df82ff021d5b53","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}