{"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/data-augmentation/papers/ran/3","list_of":"/task/data-augmentation","task":"Data Augmentation","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":3,"pages_in_order":7,"rows_per_page":100,"rows":[201,300],"of":692,"counts":{"archive_papers_tagged":8378,"with_a_code_link":3225,"where_syntology_ran_a_sample":692,"not_listed_spam_title":0,"listed":8378,"listed_where_code_ran":692,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":567,"every_run_a_failure_of_syntologys_instrument":125,"listed_with_a_run_with_no_instrument_failure":567,"listed_every_run_a_failure_of_syntologys_instrument":125,"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/data-augmentation/papers/ran/1","prev":"/task/data-augmentation/papers/ran/2","next":"/task/data-augmentation/papers/ran/4","papers":[{"url":"/paper/enhancing-sharpness-aware-optimization","slug":"enhancing-sharpness-aware-optimization","title":"Enhancing Sharpness-Aware Optimization Through Variance Suppression","date":"2023-09-27","arxiv_id":"2309.15639","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"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) · 2 unverified","sample_list":"/paper/enhancing-sharpness-aware-optimization#ran","syntology_url":"https://syntology.ai/paper/2309.15639","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.15639"}},"official":{"repos":["bingcongli/vasso"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-level-representation-learning-with","slug":"graph-level-representation-learning-with","title":"Graph-level Representation Learning with Joint-Embedding Predictive Architectures","date":"2023-09-27","arxiv_id":"2309.16014","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":7,"n_pointer_only":4,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/graph-level-representation-learning-with#ran","syntology_url":"https://syntology.ai/paper/2309.16014","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.16014"}},"official":{"repos":["geriskenderi/graph-jepa"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/finding-order-in-chaos-a-novel-data-1","slug":"finding-order-in-chaos-a-novel-data-1","title":"Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning","date":"2023-09-23","arxiv_id":"2309.13439","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/finding-order-in-chaos-a-novel-data-1#ran","syntology_url":"https://syntology.ai/paper/2309.13439","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.13439"}},"official":{"repos":["eth-siplab/Finding_Order_in_Chaos"],"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/coco-counterfactuals-automatically","slug":"coco-counterfactuals-automatically","title":"COCO-Counterfactuals: Automatically Constructed Counterfactual Examples for Image-Text Pairs","date":"2023-09-23","arxiv_id":"2309.14356","repositories_listed":0,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"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) · 3 unverified","sample_list":"/paper/coco-counterfactuals-automatically#ran","syntology_url":"https://syntology.ai/paper/2309.14356","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.14356"}},"official":null}},{"url":"/paper/mosaicfusion-diffusion-models-as-data","slug":"mosaicfusion-diffusion-models-as-data","title":"MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary Instance Segmentation","date":"2023-09-22","arxiv_id":"2309.13042","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"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) · 1 unverified","sample_list":"/paper/mosaicfusion-diffusion-models-as-data#ran","syntology_url":"https://syntology.ai/paper/2309.13042","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.13042"}},"official":{"repos":["jiahao000/mosaicfusion"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/the-reversal-curse-llms-trained-on-a-is-b","slug":"the-reversal-curse-llms-trained-on-a-is-b","title":"The Reversal Curse: LLMs trained on \"A is B\" fail to learn \"B is A\"","date":"2023-09-21","arxiv_id":"2309.12288","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":11,"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) · 2 unverified","sample_list":"/paper/the-reversal-curse-llms-trained-on-a-is-b#ran","syntology_url":"https://syntology.ai/paper/2309.12288","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.12288"}},"official":{"repos":["lukasberglund/reversal_curse"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/investigating-personalization-methods-in-text","slug":"investigating-personalization-methods-in-text","title":"Investigating Personalization Methods in Text to Music Generation","date":"2023-09-20","arxiv_id":"2309.11140","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":3,"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/investigating-personalization-methods-in-text#ran","syntology_url":"https://syntology.ai/paper/2309.11140","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.11140"}},"official":{"repos":["zelaki/DreamSound"],"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/sample-adaptive-augmentation-for-point-cloud","slug":"sample-adaptive-augmentation-for-point-cloud","title":"Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions","date":"2023-09-19","arxiv_id":"2309.10431","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":5,"phrase":"11 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; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/sample-adaptive-augmentation-for-point-cloud#ran","syntology_url":"https://syntology.ai/paper/2309.10431","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.10431"}},"official":{"repos":["roywangj/adaptpoint"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/catfood-counterfactual-augmented-training-for","slug":"catfood-counterfactual-augmented-training-for","title":"CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration","date":"2023-09-14","arxiv_id":"2309.07822","repositories_listed":1,"syntology":{"n":12,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/catfood-counterfactual-augmented-training-for#ran","syntology_url":"https://syntology.ai/paper/2309.07822","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.07822"}},"official":{"repos":["ukplab/catfood"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-better-data-exploitation-in-self","slug":"towards-better-data-exploitation-in-self","title":"Towards Better Data Exploitation in Self-Supervised Monocular Depth Estimation","date":"2023-09-11","arxiv_id":"2309.05254","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":2,"phrase":"13 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/towards-better-data-exploitation-in-self#ran","syntology_url":"https://syntology.ai/paper/2309.05254","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.05254"}},"official":{"repos":["LiuJF1226/BDEdepth"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/when-to-learn-what-model-adaptive-data","slug":"when-to-learn-what-model-adaptive-data","title":"When to Learn What: Model-Adaptive Data Augmentation Curriculum","date":"2023-09-09","arxiv_id":"2309.04747","repositories_listed":1,"syntology":{"n":24,"n_ran":23,"n_constructed":1,"n_ran_checked":4,"n_instrument":19,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":2,"n_pointer_only":24,"phrase":"23 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 1 violated, 2 with no contract checked; 19 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/when-to-learn-what-model-adaptive-data#ran","syntology_url":"https://syntology.ai/paper/2309.04747","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.04747"}},"official":{"repos":["jackhck/madaug"],"state":"official (archive's flag): 23 ran","n_ran":23,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/tsgbench-time-series-generation-benchmark","slug":"tsgbench-time-series-generation-benchmark","title":"TSGBench: Time Series Generation Benchmark","date":"2023-09-07","arxiv_id":"2309.03755","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":2,"n_no_contract":9,"n_pointer_only":14,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 2 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/tsgbench-time-series-generation-benchmark#ran","syntology_url":"https://syntology.ai/paper/2309.03755","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.03755"}},"official":{"repos":["yihaoang/tsgbench"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/fine-grained-recognition-with-learnable","slug":"fine-grained-recognition-with-learnable","title":"Fine-grained Recognition with Learnable Semantic Data Augmentation","date":"2023-09-01","arxiv_id":"2309.00399","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":7,"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) · 3 unverified","sample_list":"/paper/fine-grained-recognition-with-learnable#ran","syntology_url":"https://syntology.ai/paper/2309.00399","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.00399"}},"official":{"repos":["LeapLabTHU/LearnableISDA"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/taken-out-of-context-on-measuring-situational","slug":"taken-out-of-context-on-measuring-situational","title":"Taken out of context: On measuring situational awareness in LLMs","date":"2023-09-01","arxiv_id":"2309.00667","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":9,"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/taken-out-of-context-on-measuring-situational#ran","syntology_url":"https://syntology.ai/paper/2309.00667","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.00667"}},"official":{"repos":["asacooperstickland/situational-awareness-evals"],"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/classification-robustness-to-common-optical","slug":"classification-robustness-to-common-optical","title":"Classification robustness to common optical aberrations","date":"2023-08-29","arxiv_id":"2308.15499","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":14,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/classification-robustness-to-common-optical#ran","syntology_url":"https://syntology.ai/paper/2308.15499","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.15499"}},"official":{"repos":["patmue/classification_robustness"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/rella-retrieval-enhanced-large-language","slug":"rella-retrieval-enhanced-large-language","title":"ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation","date":"2023-08-22","arxiv_id":"2308.11131","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":9,"phrase":"8 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rella-retrieval-enhanced-large-language#ran","syntology_url":"https://syntology.ai/paper/2308.11131","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.11131"}},"official":{"repos":["lavieenrose365/rella"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/convert-contrastive-graph-clustering-with","slug":"convert-contrastive-graph-clustering-with","title":"CONVERT:Contrastive Graph Clustering with Reliable Augmentation","date":"2023-08-17","arxiv_id":"2308.08963","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":3,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/convert-contrastive-graph-clustering-with#ran","syntology_url":"https://syntology.ai/paper/2308.08963","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.08963"}},"official":{"repos":["xihongyang1999/convert"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/sslrec-a-self-supervised-learning-library-for","slug":"sslrec-a-self-supervised-learning-library-for","title":"SSLRec: A Self-Supervised Learning Framework for Recommendation","date":"2023-08-10","arxiv_id":"2308.05697","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/sslrec-a-self-supervised-learning-library-for#ran","syntology_url":"https://syntology.ai/paper/2308.05697","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.05697"}},"official":{"repos":["hkuds/sslrec"],"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/apbench-a-unified-benchmark-for-availability","slug":"apbench-a-unified-benchmark-for-availability","title":"APBench: A Unified Benchmark for Availability Poisoning Attacks and Defenses","date":"2023-08-07","arxiv_id":"2308.03258","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":4,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/apbench-a-unified-benchmark-for-availability#ran","syntology_url":"https://syntology.ai/paper/2308.03258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.03258"}},"official":{"repos":["lafeat/apbench"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/musicldm-enhancing-novelty-in-text-to-music","slug":"musicldm-enhancing-novelty-in-text-to-music","title":"MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies","date":"2023-08-03","arxiv_id":"2308.01546","repositories_listed":1,"syntology":{"n":14,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":4,"n_honours":2,"n_violates":1,"n_no_contract":6,"n_pointer_only":14,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 1 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/musicldm-enhancing-novelty-in-text-to-music#ran","syntology_url":"https://syntology.ai/paper/2308.01546","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.01546"}},"official":{"repos":["retrocirce/musicldm"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/pre-training-vision-transformers-with-very","slug":"pre-training-vision-transformers-with-very","title":"Pre-training Vision Transformers with Very Limited Synthesized Images","date":"2023-07-27","arxiv_id":"2307.14710","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/pre-training-vision-transformers-with-very#ran","syntology_url":"https://syntology.ai/paper/2307.14710","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.14710"}},"official":{"repos":["ryoo-nakamura/ofdb"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/holistic-exploration-on-universal","slug":"holistic-exploration-on-universal","title":"Holistic Exploration on Universal Decompositional Semantic Parsing: Architecture, Data Augmentation, and LLM Paradigm","date":"2023-07-25","arxiv_id":"2307.13424","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/holistic-exploration-on-universal#ran","syntology_url":"https://syntology.ai/paper/2307.13424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.13424"}},"official":{"repos":["hexuandeng/hexp4uds"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cluster-aware-semi-supervised-learning","slug":"cluster-aware-semi-supervised-learning","title":"Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering","date":"2023-07-20","arxiv_id":"2307.11030","repositories_listed":1,"syntology":{"n":18,"n_ran":15,"n_constructed":3,"n_ran_checked":14,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":5,"phrase":"15 ran (of which 3 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/cluster-aware-semi-supervised-learning#ran","syntology_url":"https://syntology.ai/paper/2307.11030","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.11030"}},"official":null}},{"url":"/paper/what-do-neural-networks-learn-in-image","slug":"what-do-neural-networks-learn-in-image","title":"What do neural networks learn in image classification? A frequency shortcut perspective","date":"2023-07-19","arxiv_id":"2307.09829","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":0,"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-do-neural-networks-learn-in-image#ran","syntology_url":"https://syntology.ai/paper/2307.09829","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.09829"}},"official":{"repos":["nis-research/nn-frequency-shortcuts"],"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/adversarial-bayesian-augmentation-for-single","slug":"adversarial-bayesian-augmentation-for-single","title":"Adversarial Bayesian Augmentation for Single-Source Domain Generalization","date":"2023-07-18","arxiv_id":"2307.09520","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"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) · 1 unverified","sample_list":"/paper/adversarial-bayesian-augmentation-for-single#ran","syntology_url":"https://syntology.ai/paper/2307.09520","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.09520"}},"official":{"repos":["shengcheng/aba"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/domain-adaptation-for-enhanced-object","slug":"domain-adaptation-for-enhanced-object","title":"Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy Weather","date":"2023-07-18","arxiv_id":"2307.09676","repositories_listed":1,"syntology":{"n":13,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":1,"n_no_contract":9,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 2 honoured, 1 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/domain-adaptation-for-enhanced-object#ran","syntology_url":"https://syntology.ai/paper/2307.09676","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.09676"}},"official":{"repos":["jinlong17/da-detect"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/altfreezing-for-more-general-video-face-1","slug":"altfreezing-for-more-general-video-face-1","title":"AltFreezing for More General Video Face Forgery Detection","date":"2023-07-17","arxiv_id":"2307.08317","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"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) · 0 unverified","sample_list":"/paper/altfreezing-for-more-general-video-face-1#ran","syntology_url":"https://syntology.ai/paper/2307.08317","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.08317"}},"official":{"repos":["zhendongwang6/altfreezing"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mixupexplainer-generalizing-explanations-for","slug":"mixupexplainer-generalizing-explanations-for","title":"MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation","date":"2023-07-15","arxiv_id":"2307.07832","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"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) · 0 unverified","sample_list":"/paper/mixupexplainer-generalizing-explanations-for#ran","syntology_url":"https://syntology.ai/paper/2307.07832","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.07832"}},"official":{"repos":["jz48/mixupexplainer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-understanding-adversarial","slug":"towards-understanding-adversarial","title":"Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability","date":"2023-07-15","arxiv_id":"2307.07873","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":7,"phrase":"12 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; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/towards-understanding-adversarial#ran","syntology_url":"https://syntology.ai/paper/2307.07873","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.07873"}},"official":{"repos":["cgcl-codes/transferattacksurrogates"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/a-synthetic-electrocardiogram-ecg-image","slug":"a-synthetic-electrocardiogram-ecg-image","title":"ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization","date":"2023-07-04","arxiv_id":"2307.01946","repositories_listed":1,"syntology":{"n":10,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":5,"n_honours":3,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 3 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/a-synthetic-electrocardiogram-ecg-image#ran","syntology_url":"https://syntology.ai/paper/2307.01946","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.01946"}},"official":{"repos":["alphanumericslab/ecg-image-kit"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/fused-gromov-wasserstein-graph-mixup-for-1","slug":"fused-gromov-wasserstein-graph-mixup-for-1","title":"Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications","date":"2023-06-28","arxiv_id":"2306.15963","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":8,"phrase":"4 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; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/fused-gromov-wasserstein-graph-mixup-for-1#ran","syntology_url":"https://syntology.ai/paper/2306.15963","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.15963"}},"official":{"repos":["arthurleom/fgwmixup"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/weighted-automata-extraction-and-explanation","slug":"weighted-automata-extraction-and-explanation","title":"Weighted Automata Extraction and Explanation of Recurrent Neural Networks for Natural Language Tasks","date":"2023-06-24","arxiv_id":"2306.14040","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/weighted-automata-extraction-and-explanation#ran","syntology_url":"https://syntology.ai/paper/2306.14040","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.14040"}},"official":{"repos":["weizeming/extract_wfa_from_rnn_for_nl"],"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/beyond-ood-state-actions-supported-cross","slug":"beyond-ood-state-actions-supported-cross","title":"Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning","date":"2023-06-22","arxiv_id":"2306.12755","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/beyond-ood-state-actions-supported-cross#ran","syntology_url":"https://syntology.ai/paper/2306.12755","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.12755"}},"official":{"repos":["thuml/SPOT"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-generalizability-of-graph-anomaly-1","slug":"improving-generalizability-of-graph-anomaly-1","title":"Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation","date":"2023-06-18","arxiv_id":"2306.10534","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/improving-generalizability-of-graph-anomaly-1#ran","syntology_url":"https://syntology.ai/paper/2306.10534","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.10534"}},"official":null}},{"url":"/paper/bridging-the-gap-between-decision-and-logits","slug":"bridging-the-gap-between-decision-and-logits","title":"Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models","date":"2023-06-15","arxiv_id":"2306.08909","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/bridging-the-gap-between-decision-and-logits#ran","syntology_url":"https://syntology.ai/paper/2306.08909","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.08909"}},"official":{"repos":["thunlp-mt/dbkd-plm"],"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/noise-stability-optimization-for-flat-minima","slug":"noise-stability-optimization-for-flat-minima","title":"Noise Stability Optimization for Finding Flat Minima: A Hessian-based Regularization Approach","date":"2023-06-14","arxiv_id":"2306.08553","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/noise-stability-optimization-for-flat-minima#ran","syntology_url":"https://syntology.ai/paper/2306.08553","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.08553"}},"official":{"repos":["virtuosoresearch/noise-stability-optimization"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/generated-graph-detection","slug":"generated-graph-detection","title":"Generated Graph Detection","date":"2023-06-13","arxiv_id":"2306.07758","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":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) · 0 unverified","sample_list":"/paper/generated-graph-detection#ran","syntology_url":"https://syntology.ai/paper/2306.07758","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07758"}},"official":{"repos":["yvonnemamama/ggd"],"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/aroid-improving-adversarial-robustness","slug":"aroid-improving-adversarial-robustness","title":"AROID: Improving Adversarial Robustness Through Online Instance-Wise Data Augmentation","date":"2023-06-12","arxiv_id":"2306.07197","repositories_listed":1,"syntology":{"n":13,"n_ran":13,"n_constructed":0,"n_ran_checked":10,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/aroid-improving-adversarial-robustness#ran","syntology_url":"https://syntology.ai/paper/2306.07197","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07197"}},"official":{"repos":["treelli/aroid"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/look-beneath-the-surface-exploiting-1","slug":"look-beneath-the-surface-exploiting-1","title":"Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL","date":"2023-06-07","arxiv_id":"2306.04220","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/look-beneath-the-surface-exploiting-1#ran","syntology_url":"https://syntology.ai/paper/2306.04220","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.04220"}},"official":{"repos":["pcheng2/tsrl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/align-distill-and-augment-everything-all-at","slug":"align-distill-and-augment-everything-all-at","title":"Flexible Distribution Alignment: Towards Long-tailed Semi-supervised Learning with Proper Calibration","date":"2023-06-07","arxiv_id":"2306.04621","repositories_listed":2,"syntology":{"n":16,"n_ran":12,"n_constructed":0,"n_ran_checked":8,"n_instrument":4,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":8,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/align-distill-and-augment-everything-all-at#ran","syntology_url":"https://syntology.ai/paper/2306.04621","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.04621"}},"official":{"repos":["emasa/adello-ltssl"],"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":["listed","official"]}}},{"url":"/paper/stabilizing-contrastive-rl-techniques-for","slug":"stabilizing-contrastive-rl-techniques-for","title":"Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data","date":"2023-06-06","arxiv_id":"2306.03346","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/stabilizing-contrastive-rl-techniques-for#ran","syntology_url":"https://syntology.ai/paper/2306.03346","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03346"}},"official":{"repos":["chongyi-zheng/stable_contrastive_rl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/conformal-prediction-with-missing-values","slug":"conformal-prediction-with-missing-values","title":"Conformal Prediction with Missing Values","date":"2023-06-05","arxiv_id":"2306.02732","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/conformal-prediction-with-missing-values#ran","syntology_url":"https://syntology.ai/paper/2306.02732","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.02732"}},"official":{"repos":["mzaffran/conformalpredictionmissingvalues"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-transformer-for-recommendation","slug":"graph-transformer-for-recommendation","title":"Graph Transformer for Recommendation","date":"2023-06-04","arxiv_id":"2306.02330","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":3,"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/graph-transformer-for-recommendation#ran","syntology_url":"https://syntology.ai/paper/2306.02330","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.02330"}},"official":{"repos":["hkuds/gformer"],"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/conditional-generation-from-unconditional","slug":"conditional-generation-from-unconditional","title":"Conditional Generation from Unconditional Diffusion Models using Denoiser Representations","date":"2023-06-02","arxiv_id":"2306.01900","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/conditional-generation-from-unconditional#ran","syntology_url":"https://syntology.ai/paper/2306.01900","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.01900"}},"official":{"repos":["cvlab-stonybrook/fewshot-conditional-diffusion"],"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/a-uniform-confidence-phenomenon-in-deep","slug":"a-uniform-confidence-phenomenon-in-deep","title":"On the Limitations of Temperature Scaling for Distributions with Overlaps","date":"2023-06-01","arxiv_id":"2306.00740","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/a-uniform-confidence-phenomenon-in-deep#ran","syntology_url":"https://syntology.ai/paper/2306.00740","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00740"}},"official":{"repos":["2014mchidamb/temp-scaling-limitations"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/improved-probabilistic-image-text","slug":"improved-probabilistic-image-text","title":"Improved Probabilistic Image-Text Representations","date":"2023-05-29","arxiv_id":"2305.18171","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"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) · 2 unverified","sample_list":"/paper/improved-probabilistic-image-text#ran","syntology_url":"https://syntology.ai/paper/2305.18171","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18171"}},"official":{"repos":["naver-ai/pcmepp"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/crest-a-joint-framework-for-rationalization","slug":"crest-a-joint-framework-for-rationalization","title":"CREST: A Joint Framework for Rationalization and Counterfactual Text Generation","date":"2023-05-26","arxiv_id":"2305.17075","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/crest-a-joint-framework-for-rationalization#ran","syntology_url":"https://syntology.ai/paper/2305.17075","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.17075"}},"official":{"repos":["deep-spin/crest"],"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/training-on-thin-air-improve-image","slug":"training-on-thin-air-improve-image","title":"Training on Thin Air: Improve Image Classification with Generated Data","date":"2023-05-24","arxiv_id":"2305.15316","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":4,"phrase":"8 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/training-on-thin-air-improve-image#ran","syntology_url":"https://syntology.ai/paper/2305.15316","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.15316"}},"official":{"repos":["yongchao97/diffusion_inversion"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/making-more-of-little-data-improving-low","slug":"making-more-of-little-data-improving-low","title":"Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation","date":"2023-05-18","arxiv_id":"2305.10951","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":0,"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/making-more-of-little-data-improving-low#ran","syntology_url":"https://syntology.ai/paper/2305.10951","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.10951"}},"official":{"repos":["bartelds/asr-augmentation"],"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/rethinking-data-augmentation-for-tabular-data","slug":"rethinking-data-augmentation-for-tabular-data","title":"Rethinking Data Augmentation for Tabular Data in Deep Learning","date":"2023-05-17","arxiv_id":"2305.10308","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/rethinking-data-augmentation-for-tabular-data#ran","syntology_url":"https://syntology.ai/paper/2305.10308","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.10308"}},"official":{"repos":["somaonishi/mtr"],"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/bidirectional-generative-framework-for-cross","slug":"bidirectional-generative-framework-for-cross","title":"Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis","date":"2023-05-16","arxiv_id":"2305.09509","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":12,"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) · 5 unverified","sample_list":"/paper/bidirectional-generative-framework-for-cross#ran","syntology_url":"https://syntology.ai/paper/2305.09509","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.09509"}},"official":{"repos":["damo-nlp-sg/bgca"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/contrastive-domain-generalization-via-logit","slug":"contrastive-domain-generalization-via-logit","title":"Consistency Regularization for Domain Generalization with Logit Attribution Matching","date":"2023-05-13","arxiv_id":"2305.07888","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"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) · 4 unverified","sample_list":"/paper/contrastive-domain-generalization-via-logit#ran","syntology_url":"https://syntology.ai/paper/2305.07888","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.07888"}},"official":{"repos":["gaohan123/lam"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/scene-self-labeled-counterfactuals-for","slug":"scene-self-labeled-counterfactuals-for","title":"SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples","date":"2023-05-13","arxiv_id":"2305.07984","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/scene-self-labeled-counterfactuals-for#ran","syntology_url":"https://syntology.ai/paper/2305.07984","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.07984"}},"official":{"repos":["deqingfu/scene"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/the-robustness-of-computer-vision-models","slug":"the-robustness-of-computer-vision-models","title":"A Survey on the Robustness of Computer Vision Models against Common Corruptions","date":"2023-05-10","arxiv_id":"2305.06024","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/the-robustness-of-computer-vision-models#ran","syntology_url":"https://syntology.ai/paper/2305.06024","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.06024"}},"official":{"repos":["nis-research/corruptionbenchcv"],"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/semantic-aware-generation-of-multi-view","slug":"semantic-aware-generation-of-multi-view","title":"Semantic-aware Generation of Multi-view Portrait Drawings","date":"2023-05-04","arxiv_id":"2305.02618","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":9,"n_ran_checked":10,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":13,"phrase":"11 ran (of which 9 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/semantic-aware-generation-of-multi-view#ran","syntology_url":"https://syntology.ai/paper/2305.02618","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.02618"}},"official":{"repos":["aiart-hdu/sage"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":9,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/part-aware-contrastive-learning-for-self","slug":"part-aware-contrastive-learning-for-self","title":"Part Aware Contrastive Learning for Self-Supervised Action Recognition","date":"2023-05-01","arxiv_id":"2305.00666","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":12,"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) · 6 unverified","sample_list":"/paper/part-aware-contrastive-learning-for-self#ran","syntology_url":"https://syntology.ai/paper/2305.00666","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.00666"}},"official":{"repos":["githubofhyl97/skeattnclr"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/is-a-prompt-and-a-few-samples-all-you-need","slug":"is-a-prompt-and-a-few-samples-all-you-need","title":"The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Tasks","date":"2023-04-26","arxiv_id":"2304.13861","repositories_listed":2,"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/is-a-prompt-and-a-few-samples-all-you-need#ran","syntology_url":"https://syntology.ai/paper/2304.13861","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.13861"}},"official":{"repos":["andersgiovanni/worker_vs_gpt","AGMoller/worker_vs_gpt"],"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/mixpro-data-augmentation-with-maskmix-and","slug":"mixpro-data-augmentation-with-maskmix-and","title":"MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer","date":"2023-04-24","arxiv_id":"2304.12043","repositories_listed":1,"syntology":{"n":18,"n_ran":16,"n_constructed":0,"n_ran_checked":9,"n_instrument":7,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":8,"phrase":"16 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; 7 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mixpro-data-augmentation-with-maskmix-and#ran","syntology_url":"https://syntology.ai/paper/2304.12043","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.12043"}},"official":{"repos":["fistyee/mixpro"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/ttida-controllable-generative-data","slug":"ttida-controllable-generative-data","title":"TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models","date":"2023-04-18","arxiv_id":"2304.08821","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":2,"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/ttida-controllable-generative-data#ran","syntology_url":"https://syntology.ai/paper/2304.08821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.08821"}},"official":{"repos":["yuweiyin/ttida"],"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/meta-optimized-contrastive-learning-for","slug":"meta-optimized-contrastive-learning-for","title":"Meta-optimized Contrastive Learning for Sequential Recommendation","date":"2023-04-16","arxiv_id":"2304.07763","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/meta-optimized-contrastive-learning-for#ran","syntology_url":"https://syntology.ai/paper/2304.07763","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.07763"}},"official":{"repos":["qinhsiu/mclrec"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hierarchical-supervision-and-shuffle-data","slug":"hierarchical-supervision-and-shuffle-data","title":"Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection","date":"2023-04-04","arxiv_id":"2304.01464","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/hierarchical-supervision-and-shuffle-data#ran","syntology_url":"https://syntology.ai/paper/2304.01464","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.01464"}},"official":{"repos":["azhuantou/hssda"],"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/astroformer-more-data-might-not-be-all-you","slug":"astroformer-more-data-might-not-be-all-you","title":"Astroformer: More Data Might not be all you need for Classification","date":"2023-04-03","arxiv_id":"2304.05350","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":0,"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/astroformer-more-data-might-not-be-all-you#ran","syntology_url":"https://syntology.ai/paper/2304.05350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.05350"}},"official":{"repos":["Rishit-dagli/Astroformer"],"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/mixed-autoencoder-for-self-supervised-visual","slug":"mixed-autoencoder-for-self-supervised-visual","title":"Mixed Autoencoder for Self-supervised Visual Representation Learning","date":"2023-03-30","arxiv_id":"2303.17152","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":5,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":8,"phrase":"7 ran (of which 5 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) · 1 unverified","sample_list":"/paper/mixed-autoencoder-for-self-supervised-visual#ran","syntology_url":"https://syntology.ai/paper/2303.17152","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.17152"}},"official":null}},{"url":"/paper/de-coupling-and-de-positioning-dense-self","slug":"de-coupling-and-de-positioning-dense-self","title":"De-coupling and De-positioning Dense Self-supervised Learning","date":"2023-03-29","arxiv_id":"2303.16947","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":5,"n_instrument":5,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/de-coupling-and-de-positioning-dense-self#ran","syntology_url":"https://syntology.ai/paper/2303.16947","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.16947"}},"official":{"repos":["ztt1024/densessl"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-the-unlearnable-adversarial","slug":"learning-the-unlearnable-adversarial","title":"Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example Attacks","date":"2023-03-27","arxiv_id":"2303.15127","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/learning-the-unlearnable-adversarial#ran","syntology_url":"https://syntology.ai/paper/2303.15127","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.15127"}},"official":{"repos":["lafeat/ueraser"],"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/debiased-contrastive-learning-for-sequential","slug":"debiased-contrastive-learning-for-sequential","title":"Debiased Contrastive Learning for Sequential Recommendation","date":"2023-03-21","arxiv_id":"2303.11780","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/debiased-contrastive-learning-for-sequential#ran","syntology_url":"https://syntology.ai/paper/2303.11780","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.11780"}},"official":{"repos":["hkuds/dcrec"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/svdiff-compact-parameter-space-for-diffusion","slug":"svdiff-compact-parameter-space-for-diffusion","title":"SVDiff: Compact Parameter Space for Diffusion Fine-Tuning","date":"2023-03-20","arxiv_id":"2303.11305","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/svdiff-compact-parameter-space-for-diffusion#ran","syntology_url":"https://syntology.ai/paper/2303.11305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.11305"}},"official":null}},{"url":"/paper/mixcycle-mixup-assisted-semi-supervised-3d","slug":"mixcycle-mixup-assisted-semi-supervised-3d","title":"MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency","date":"2023-03-16","arxiv_id":"2303.09219","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/mixcycle-mixup-assisted-semi-supervised-3d#ran","syntology_url":"https://syntology.ai/paper/2303.09219","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.09219"}},"official":{"repos":["mumuqiao/mixcycle"],"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/mseg3d-multi-modal-3d-semantic-segmentation","slug":"mseg3d-multi-modal-3d-semantic-segmentation","title":"MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Driving","date":"2023-03-15","arxiv_id":"2303.08600","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":1,"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/mseg3d-multi-modal-3d-semantic-segmentation#ran","syntology_url":"https://syntology.ai/paper/2303.08600","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.08600"}},"official":{"repos":["jialeli1/lidarseg3d"],"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/spidermesh-spatial-aware-demand-guided","slug":"spidermesh-spatial-aware-demand-guided","title":"SpiderMesh: Spatial-aware Demand-guided Recursive Meshing for RGB-T Semantic Segmentation","date":"2023-03-15","arxiv_id":"2303.08692","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/spidermesh-spatial-aware-demand-guided#ran","syntology_url":"https://syntology.ai/paper/2303.08692","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.08692"}},"official":{"repos":["leofansq/spidermesh"],"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/rotation-invariant-transformer-for-point","slug":"rotation-invariant-transformer-for-point","title":"Rotation-Invariant Transformer for Point Cloud Matching","date":"2023-03-14","arxiv_id":"2303.08231","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"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) · 1 unverified","sample_list":"/paper/rotation-invariant-transformer-for-point#ran","syntology_url":"https://syntology.ai/paper/2303.08231","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.08231"}},"official":{"repos":["haoyu94/roitr"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-democratizing-joint-embedding-self","slug":"towards-democratizing-joint-embedding-self","title":"Towards Democratizing Joint-Embedding Self-Supervised Learning","date":"2023-03-03","arxiv_id":"2303.01986","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":2,"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-democratizing-joint-embedding-self#ran","syntology_url":"https://syntology.ai/paper/2303.01986","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.01986"}},"official":{"repos":["facebookresearch/ffcv-ssl"],"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/self-supervised-action-representation","slug":"self-supervised-action-representation","title":"Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences","date":"2023-02-17","arxiv_id":"2302.09018","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-supervised-action-representation#ran","syntology_url":"https://syntology.ai/paper/2302.09018","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.09018"}},"official":{"repos":["yujieouo/pstl"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/urcdc-depth-uncertainty-rectified-cross","slug":"urcdc-depth-uncertainty-rectified-cross","title":"URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation","date":"2023-02-16","arxiv_id":"2302.08149","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/urcdc-depth-uncertainty-rectified-cross#ran","syntology_url":"https://syntology.ai/paper/2302.08149","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.08149"}},"official":{"repos":["shuweishao/urcdc-depth"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/lightgcl-simple-yet-effective-graph","slug":"lightgcl-simple-yet-effective-graph","title":"LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation","date":"2023-02-16","arxiv_id":"2302.08191","repositories_listed":1,"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":2,"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/lightgcl-simple-yet-effective-graph#ran","syntology_url":"https://syntology.ai/paper/2302.08191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.08191"}},"official":{"repos":["hkuds/lightgcl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-performance-improving-code-edits","slug":"learning-performance-improving-code-edits","title":"Learning Performance-Improving Code Edits","date":"2023-02-15","arxiv_id":"2302.07867","repositories_listed":2,"syntology":{"n":19,"n_ran":8,"n_constructed":0,"n_ran_checked":3,"n_instrument":5,"n_unverified":11,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":19,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 5 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/learning-performance-improving-code-edits#ran","syntology_url":"https://syntology.ai/paper/2302.07867","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.07867"}},"official":{"repos":["madaan/pie-perf"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/cuda-curriculum-of-data-augmentation-for-long","slug":"cuda-curriculum-of-data-augmentation-for-long","title":"CUDA: Curriculum of Data Augmentation for Long-Tailed Recognition","date":"2023-02-10","arxiv_id":"2302.05499","repositories_listed":1,"syntology":{"n":20,"n_ran":18,"n_constructed":0,"n_ran_checked":1,"n_instrument":17,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":20,"phrase":"18 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; 17 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/cuda-curriculum-of-data-augmentation-for-long#ran","syntology_url":"https://syntology.ai/paper/2302.05499","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.05499"}},"official":{"repos":["sumyeongahn/cuda_ltr"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/mask-conditional-synthetic-satellite-imagery","slug":"mask-conditional-synthetic-satellite-imagery","title":"Mask Conditional Synthetic Satellite Imagery","date":"2023-02-08","arxiv_id":"2302.04305","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/mask-conditional-synthetic-satellite-imagery#ran","syntology_url":"https://syntology.ai/paper/2302.04305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.04305"}},"official":{"repos":["ms-synthetic-satellite-image/synthetic-satellite-imagery"],"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/osrt-omnidirectional-image-super-resolution","slug":"osrt-omnidirectional-image-super-resolution","title":"OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer","date":"2023-02-07","arxiv_id":"2302.03453","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/osrt-omnidirectional-image-super-resolution#ran","syntology_url":"https://syntology.ai/paper/2302.03453","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.03453"}},"official":{"repos":["fanghua-yu/osrt"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/effective-data-augmentation-with-diffusion","slug":"effective-data-augmentation-with-diffusion","title":"Effective Data Augmentation With Diffusion Models","date":"2023-02-07","arxiv_id":"2302.07944","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":1,"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/effective-data-augmentation-with-diffusion#ran","syntology_url":"https://syntology.ai/paper/2302.07944","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.07944"}},"official":{"repos":["brandontrabucco/da-fusion"],"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/learning-representations-of-bi-level","slug":"learning-representations-of-bi-level","title":"Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction","date":"2023-02-06","arxiv_id":"2302.02601","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"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) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/learning-representations-of-bi-level#ran","syntology_url":"https://syntology.ai/paper/2302.02601","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.02601"}},"official":{"repos":["bdi-lab/bive"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/contrastive-learning-with-consistent","slug":"contrastive-learning-with-consistent","title":"Contrastive Learning with Consistent Representations","date":"2023-02-03","arxiv_id":"2302.01541","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":2,"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/contrastive-learning-with-consistent#ran","syntology_url":"https://syntology.ai/paper/2302.01541","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.01541"}},"official":{"repos":["zihuwang97/cocor"],"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/winning-solution-of-real-robot-challenge-iii","slug":"winning-solution-of-real-robot-challenge-iii","title":"Identifying Expert Behavior in Offline Training Datasets Improves Behavioral Cloning of Robotic Manipulation Policies","date":"2023-01-30","arxiv_id":"2301.13019","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/winning-solution-of-real-robot-challenge-iii#ran","syntology_url":"https://syntology.ai/paper/2301.13019","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.13019"}},"official":{"repos":["wq13552463699/real-robot-challenge-2022"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/deja-vu-continual-model-generalization-for","slug":"deja-vu-continual-model-generalization-for","title":"DEJA VU: Continual Model Generalization For Unseen Domains","date":"2023-01-25","arxiv_id":"2301.10418","repositories_listed":2,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":10,"phrase":"7 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; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/deja-vu-continual-model-generalization-for#ran","syntology_url":"https://syntology.ai/paper/2301.10418","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.10418"}},"official":{"repos":["dawnliu35/dejavu-ratp","sonyai/ratp"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/data-augmentation-alone-can-improve","slug":"data-augmentation-alone-can-improve","title":"Data Augmentation Alone Can Improve Adversarial Training","date":"2023-01-24","arxiv_id":"2301.09879","repositories_listed":1,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":13,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":1,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/data-augmentation-alone-can-improve#ran","syntology_url":"https://syntology.ai/paper/2301.09879","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.09879"}},"official":{"repos":["treelli/da-alone-improves-at"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-training-under-limited-resources","slug":"efficient-training-under-limited-resources","title":"Efficient Training Under Limited Resources","date":"2023-01-23","arxiv_id":"2301.09264","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/efficient-training-under-limited-resources#ran","syntology_url":"https://syntology.ai/paper/2301.09264","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.09264"}},"official":{"repos":["dounialakhmiri/iclr_haet2021"],"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/excelformer-a-neural-network-surpassing-gbdts","slug":"excelformer-a-neural-network-surpassing-gbdts","title":"ExcelFormer: A neural network surpassing GBDTs on tabular data","date":"2023-01-07","arxiv_id":"2301.02819","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/excelformer-a-neural-network-surpassing-gbdts#ran","syntology_url":"https://syntology.ai/paper/2301.02819","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.02819"}},"official":{"repos":["whatashot/excelformer"],"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/naq-leveraging-narrations-as-queries-to","slug":"naq-leveraging-narrations-as-queries-to","title":"NaQ: Leveraging Narrations as Queries to Supervise Episodic Memory","date":"2023-01-02","arxiv_id":"2301.00746","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":11,"n_instrument":1,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":9,"n_pointer_only":4,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 2 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/naq-leveraging-narrations-as-queries-to#ran","syntology_url":"https://syntology.ai/paper/2301.00746","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.00746"}},"official":{"repos":["srama2512/NaQ"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/cap4video-what-can-auxiliary-captions-do-for","slug":"cap4video-what-can-auxiliary-captions-do-for","title":"Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval?","date":"2022-12-31","arxiv_id":"2301.00184","repositories_listed":4,"syntology":{"n":25,"n_ran":20,"n_constructed":0,"n_ran_checked":12,"n_instrument":8,"n_unverified":5,"n_honours":2,"n_violates":1,"n_no_contract":9,"n_pointer_only":11,"phrase":"20 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 2 honoured, 1 violated, 9 with no contract checked; 8 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/cap4video-what-can-auxiliary-captions-do-for#ran","syntology_url":"https://syntology.ai/paper/2301.00184","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.00184"}},"official":{"repos":["whwu95/Cap4Video"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/styletts-vc-one-shot-voice-conversion-by","slug":"styletts-vc-one-shot-voice-conversion-by","title":"StyleTTS-VC: One-Shot Voice Conversion by Knowledge Transfer from Style-Based TTS Models","date":"2022-12-29","arxiv_id":"2212.14227","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":5,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/styletts-vc-one-shot-voice-conversion-by#ran","syntology_url":"https://syntology.ai/paper/2212.14227","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.14227"}},"official":{"repos":["yl4579/StyleTTS-VC"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-multimodal-data-augmentation-in","slug":"learning-multimodal-data-augmentation-in","title":"Learning Multimodal Data Augmentation in Feature Space","date":"2022-12-29","arxiv_id":"2212.14453","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":1,"n_ran_checked":3,"n_instrument":6,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":1,"n_pointer_only":0,"phrase":"9 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 2 violated, 1 with no contract checked; 6 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-multimodal-data-augmentation-in#ran","syntology_url":"https://syntology.ai/paper/2212.14453","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.14453"}},"official":{"repos":["lzcemma/lemda"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":1,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/toward-a-unified-framework-for-unsupervised","slug":"toward-a-unified-framework-for-unsupervised","title":"Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training Framework","date":"2022-12-20","arxiv_id":"2212.10097","repositories_listed":2,"syntology":{"n":20,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":0,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/toward-a-unified-framework-for-unsupervised#ran","syntology_url":"https://syntology.ai/paper/2212.10097","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.10097"}},"official":{"repos":["leezythu/uctr","leezythu/uctr-st"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/disco-distilling-phrasal-counterfactuals-with","slug":"disco-distilling-phrasal-counterfactuals-with","title":"DISCO: Distilling Counterfactuals with Large Language Models","date":"2022-12-20","arxiv_id":"2212.10534","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/disco-distilling-phrasal-counterfactuals-with#ran","syntology_url":"https://syntology.ai/paper/2212.10534","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.10534"}},"official":{"repos":["eric11eca/disco"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/randomized-quantization-for-data-agnostic","slug":"randomized-quantization-for-data-agnostic","title":"Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning","date":"2022-12-19","arxiv_id":"2212.08663","repositories_listed":4,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/randomized-quantization-for-data-agnostic#ran","syntology_url":"https://syntology.ai/paper/2212.08663","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.08663"}},"official":{"repos":["microsoft/random_quantize"],"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/better-may-not-be-fairer-can-data","slug":"better-may-not-be-fairer-can-data","title":"Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image Classification","date":"2022-12-16","arxiv_id":"2212.08649","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/better-may-not-be-fairer-can-data#ran","syntology_url":"https://syntology.ai/paper/2212.08649","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.08649"}},"official":{"repos":["charismaticchiu/Better-May-Not-Be-Fairer-A-Study-Study-on-Subgroup-Discrepancy-in-Image-Classification"],"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/robust-policy-optimization-in-deep","slug":"robust-policy-optimization-in-deep","title":"Robust Policy Optimization in Deep Reinforcement Learning","date":"2022-12-14","arxiv_id":"2212.07536","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/robust-policy-optimization-in-deep#ran","syntology_url":"https://syntology.ai/paper/2212.07536","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.07536"}},"official":{"repos":["vwxyzjn/cleanrl"],"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/x-paste-revisit-copy-paste-at-scale-with-clip","slug":"x-paste-revisit-copy-paste-at-scale-with-clip","title":"X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion","date":"2022-12-07","arxiv_id":"2212.03863","repositories_listed":2,"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/x-paste-revisit-copy-paste-at-scale-with-clip#ran","syntology_url":"https://syntology.ai/paper/2212.03863","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.03863"}},"official":{"repos":["yoctta/xpaste"],"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/region-conditioned-orthogonal-3d-u-net-for","slug":"region-conditioned-orthogonal-3d-u-net-for","title":"Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition","date":"2022-12-05","arxiv_id":"2212.02059","repositories_listed":2,"syntology":{"n":20,"n_ran":18,"n_constructed":5,"n_ran_checked":10,"n_instrument":8,"n_unverified":2,"n_honours":2,"n_violates":1,"n_no_contract":7,"n_pointer_only":20,"phrase":"18 ran (of which 5 constructed an object rather than computing a result; 10 with no instrument failure: 2 honoured, 1 violated, 7 with no contract checked; 8 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/region-conditioned-orthogonal-3d-u-net-for#ran","syntology_url":"https://syntology.ai/paper/2212.02059","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.02059"}},"official":{"repos":["hyeonjeong1/22-neurips-competition-baseline","hyeonjeong1/22-NeurIPS-Weather4Cast"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":5,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/objectstitch-generative-object-compositing","slug":"objectstitch-generative-object-compositing","title":"ObjectStitch: Generative Object Compositing","date":"2022-12-02","arxiv_id":"2212.00932","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"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; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/objectstitch-generative-object-compositing#ran","syntology_url":"https://syntology.ai/paper/2212.00932","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.00932"}},"official":null}},{"url":"/paper/diggan-discriminator-gradient-gap","slug":"diggan-discriminator-gradient-gap","title":"DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data","date":"2022-11-27","arxiv_id":"2211.14694","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/diggan-discriminator-gradient-gap#ran","syntology_url":"https://syntology.ai/paper/2211.14694","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.14694"}},"official":{"repos":["ailsaf/diggan"],"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"]}}}],"record_sha256":"4d9b2561031892a69be04fc4b1e4db2430739215de34beb62d49ccf36ff25b0a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}