{"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/binary-classification/papers/ran/1","list_of":"/task/binary-classification","task":"Binary Classification","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":2,"rows_per_page":100,"rows":[1,100],"of":115,"counts":{"archive_papers_tagged":2574,"with_a_code_link":710,"where_syntology_ran_a_sample":115,"not_listed_spam_title":0,"listed":2574,"listed_where_code_ran":115,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":100,"every_run_a_failure_of_syntologys_instrument":15,"listed_with_a_run_with_no_instrument_failure":100,"listed_every_run_a_failure_of_syntologys_instrument":15,"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/binary-classification/papers/ran/1","prev":null,"next":"/task/binary-classification/papers/ran/2","papers":[{"url":"/paper/2506-08762","slug":"2506-08762","title":"EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements","date":"2025-06-10","arxiv_id":"2506.08762","repositories_listed":2,"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":0,"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/2506-08762#ran","syntology_url":"https://syntology.ai/paper/2506.08762","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.08762"}},"official":{"repos":["sakanaai/edinet-bench","sakanaai/edinet2dataset"],"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/dison-decentralized-isolation-networks-for","slug":"dison-decentralized-isolation-networks-for","title":"DIsoN: Decentralized Isolation Networks for Out-of-Distribution Detection in Medical Imaging","date":"2025-06-10","arxiv_id":"2506.09024","repositories_listed":0,"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/dison-decentralized-isolation-networks-for#ran","syntology_url":"https://syntology.ai/paper/2506.09024","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.09024"}},"official":null}},{"url":"/paper/zeroth-order-optimization-finds-flat-minima","slug":"zeroth-order-optimization-finds-flat-minima","title":"Zeroth-Order Optimization Finds Flat Minima","date":"2025-06-05","arxiv_id":"2506.05454","repositories_listed":0,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":3,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 3 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/zeroth-order-optimization-finds-flat-minima#ran","syntology_url":"https://syntology.ai/paper/2506.05454","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.05454"}},"official":null}},{"url":"/paper/busterx-mllm-powered-ai-generated-video","slug":"busterx-mllm-powered-ai-generated-video","title":"BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation","date":"2025-05-19","arxiv_id":"2505.12620","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/busterx-mllm-powered-ai-generated-video#ran","syntology_url":"https://syntology.ai/paper/2505.12620","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.12620"}},"official":{"repos":["l8cv/busterx"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/comrecgc-global-graph-counterfactual","slug":"comrecgc-global-graph-counterfactual","title":"COMRECGC: Global Graph Counterfactual Explainer through Common Recourse","date":"2025-05-11","arxiv_id":"2505.07081","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/comrecgc-global-graph-counterfactual#ran","syntology_url":"https://syntology.ai/paper/2505.07081","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.07081"}},"official":{"repos":["ssggreg/comrecgc"],"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/rethinking-vision-language-model-in-face","slug":"rethinking-vision-language-model-in-face","title":"Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector","date":"2025-03-26","arxiv_id":"2503.20188","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/rethinking-vision-language-model-in-face#ran","syntology_url":"https://syntology.ai/paper/2503.20188","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.20188"}},"official":{"repos":["chelsea234/m2f2_det"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/vlrmbench-a-comprehensive-and-challenging","slug":"vlrmbench-a-comprehensive-and-challenging","title":"VLRMBench: A Comprehensive and Challenging Benchmark for Vision-Language Reward Models","date":"2025-03-10","arxiv_id":"2503.07478","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/vlrmbench-a-comprehensive-and-challenging#ran","syntology_url":"https://syntology.ai/paper/2503.07478","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.07478"}},"official":{"repos":["jcruan519/vlrmbench"],"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/the-effect-of-optimal-self-distillation-in","slug":"the-effect-of-optimal-self-distillation-in","title":"The Effect of Optimal Self-Distillation in Noisy Gaussian Mixture Model","date":"2025-01-27","arxiv_id":"2501.16226","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/the-effect-of-optimal-self-distillation-in#ran","syntology_url":"https://syntology.ai/paper/2501.16226","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.16226"}},"official":{"repos":["taka255/self-distillation-analysis"],"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/self-supervised-learning-for-detecting-ai","slug":"self-supervised-learning-for-detecting-ai","title":"Self-Supervised Learning for Detecting AI-Generated Faces as Anomalies","date":"2025-01-04","arxiv_id":"2501.02207","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":4,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 4 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-supervised-learning-for-detecting-ai#ran","syntology_url":"https://syntology.ai/paper/2501.02207","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.02207"}},"official":{"repos":["mzmmsec/aigfd_exif"],"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/lie-equivariant-quantum-graph-neural-networks","slug":"lie-equivariant-quantum-graph-neural-networks","title":"Lie-Equivariant Quantum Graph Neural Networks","date":"2024-11-22","arxiv_id":"2411.15315","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"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) · 4 unverified","sample_list":"/paper/lie-equivariant-quantum-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2411.15315","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.15315"}},"official":{"repos":["ml4sci/qmlhep"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/detective-detecting-ai-generated-text-via","slug":"detective-detecting-ai-generated-text-via","title":"DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learning","date":"2024-10-28","arxiv_id":"2410.20964","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/detective-detecting-ai-generated-text-via#ran","syntology_url":"https://syntology.ai/paper/2410.20964","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.20964"}},"official":{"repos":["heyongxin233/detective"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/how-eeg-preprocessing-shapes-decoding","slug":"how-eeg-preprocessing-shapes-decoding","title":"How EEG preprocessing shapes decoding performance","date":"2024-10-18","arxiv_id":"2410.14453","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/how-eeg-preprocessing-shapes-decoding#ran","syntology_url":"https://syntology.ai/paper/2410.14453","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.14453"}},"official":{"repos":["kesslerr/m4d"],"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/meta-chunking-learning-efficient-text","slug":"meta-chunking-learning-efficient-text","title":"Meta-Chunking: Learning Text Segmentation and Semantic Completion via Logical Perception","date":"2024-10-16","arxiv_id":"2410.12788","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":0,"n_no_contract":4,"n_pointer_only":1,"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) · 0 unverified","sample_list":"/paper/meta-chunking-learning-efficient-text#ran","syntology_url":"https://syntology.ai/paper/2410.12788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.12788"}},"official":{"repos":["IAAR-Shanghai/Meta-Chunking"],"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/hlm-cite-hybrid-language-model-workflow-for","slug":"hlm-cite-hybrid-language-model-workflow-for","title":"HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction","date":"2024-10-10","arxiv_id":"2410.09112","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/hlm-cite-hybrid-language-model-workflow-for#ran","syntology_url":"https://syntology.ai/paper/2410.09112","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.09112"}},"official":{"repos":["tsinghua-fib-lab/H-LM"],"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/abcfair-an-adaptable-benchmark-approach-for","slug":"abcfair-an-adaptable-benchmark-approach-for","title":"ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methods","date":"2024-09-25","arxiv_id":"2409.16965","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/abcfair-an-adaptable-benchmark-approach-for#ran","syntology_url":"https://syntology.ai/paper/2409.16965","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.16965"}},"official":{"repos":["aida-ugent/abcfair"],"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/thames-an-end-to-end-tool-for-hallucination","slug":"thames-an-end-to-end-tool-for-hallucination","title":"THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models","date":"2024-09-17","arxiv_id":"2409.11353","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/thames-an-end-to-end-tool-for-hallucination#ran","syntology_url":"https://syntology.ai/paper/2409.11353","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.11353"}},"official":{"repos":["holistic-ai/THaMES"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/pmlbmini-a-tabular-classification-benchmark","slug":"pmlbmini-a-tabular-classification-benchmark","title":"PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications","date":"2024-09-03","arxiv_id":"2409.01635","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/pmlbmini-a-tabular-classification-benchmark#ran","syntology_url":"https://syntology.ai/paper/2409.01635","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.01635"}},"official":{"repos":["ricardoknauer/tabmini"],"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/llm-detectaive-a-tool-for-fine-grained","slug":"llm-detectaive-a-tool-for-fine-grained","title":"LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection","date":"2024-08-08","arxiv_id":"2408.04284","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"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) · 2 unverified","sample_list":"/paper/llm-detectaive-a-tool-for-fine-grained#ran","syntology_url":"https://syntology.ai/paper/2408.04284","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.04284"}},"official":{"repos":["mbzuai-nlp/llm-detectaive"],"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/the-reason-behind-good-or-bad-towards-a","slug":"the-reason-behind-good-or-bad-towards-a","title":"LLM Critics Help Catch Bugs in Mathematics: Towards a Better Mathematical Verifier with Natural Language Feedback","date":"2024-06-20","arxiv_id":"2406.14024","repositories_listed":1,"syntology":{"n":23,"n_ran":22,"n_constructed":0,"n_ran_checked":18,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":17,"n_pointer_only":23,"phrase":"22 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 1 violated, 17 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/the-reason-behind-good-or-bad-towards-a#ran","syntology_url":"https://syntology.ai/paper/2406.14024","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14024"}},"official":{"repos":["kbsdjames/math-minos"],"state":"official (archive's flag): 22 ran","n_ran":22,"n_constructed":0,"n_ran_no_instrument_failure":18,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/probing-the-decision-boundaries-of-in-context","slug":"probing-the-decision-boundaries-of-in-context","title":"Probing the Decision Boundaries of In-context Learning in Large Language Models","date":"2024-06-17","arxiv_id":"2406.11233","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":8,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/probing-the-decision-boundaries-of-in-context#ran","syntology_url":"https://syntology.ai/paper/2406.11233","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.11233"}},"official":{"repos":["siyan-zhao/ICL_decision_boundary"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/split-and-fit-learning-b-reps-via-structure","slug":"split-and-fit-learning-b-reps-via-structure","title":"Split-and-Fit: Learning B-Reps via Structure-Aware Voronoi Partitioning","date":"2024-06-07","arxiv_id":"2406.05261","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/split-and-fit-learning-b-reps-via-structure#ran","syntology_url":"https://syntology.ai/paper/2406.05261","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.05261"}},"official":{"repos":["yilinliu77/nvdnet"],"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/glauber-generative-model-discrete-diffusion","slug":"glauber-generative-model-discrete-diffusion","title":"Glauber Generative Model: Discrete Diffusion Models via Binary Classification","date":"2024-05-27","arxiv_id":"2405.17035","repositories_listed":0,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/glauber-generative-model-discrete-diffusion#ran","syntology_url":"https://syntology.ai/paper/2405.17035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17035"}},"official":null}},{"url":"/paper/semeval-2024-task-8-multidomain-multimodel","slug":"semeval-2024-task-8-multidomain-multimodel","title":"SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection","date":"2024-04-22","arxiv_id":"2404.14183","repositories_listed":1,"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":1,"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/semeval-2024-task-8-multidomain-multimodel#ran","syntology_url":"https://syntology.ai/paper/2404.14183","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.14183"}},"official":null}},{"url":"/paper/neuro-inspired-information-theoretic","slug":"neuro-inspired-information-theoretic","title":"Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning","date":"2024-04-15","arxiv_id":"2404.09403","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/neuro-inspired-information-theoretic#ran","syntology_url":"https://syntology.ai/paper/2404.09403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.09403"}},"official":{"repos":["joshuaxiao98/ithp"],"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/pytorch-frame-a-modular-framework-for-multi","slug":"pytorch-frame-a-modular-framework-for-multi","title":"PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning","date":"2024-03-31","arxiv_id":"2404.00776","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pytorch-frame-a-modular-framework-for-multi#ran","syntology_url":"https://syntology.ai/paper/2404.00776","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.00776"}},"official":{"repos":["pyg-team/pytorch-frame"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/better-than-classical-the-subtle-art-of","slug":"better-than-classical-the-subtle-art-of","title":"Better than classical? The subtle art of benchmarking quantum machine learning models","date":"2024-03-11","arxiv_id":"2403.07059","repositories_listed":3,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/better-than-classical-the-subtle-art-of#ran","syntology_url":"https://syntology.ai/paper/2403.07059","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.07059"}},"official":{"repos":["xanaduai/qml-benchmarks"],"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":["listed","official"]}}},{"url":"/paper/guardt2i-defending-text-to-image-models-from","slug":"guardt2i-defending-text-to-image-models-from","title":"GuardT2I: Defending Text-to-Image Models from Adversarial Prompts","date":"2024-03-03","arxiv_id":"2403.01446","repositories_listed":3,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":2,"n_no_contract":4,"n_pointer_only":12,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 2 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/guardt2i-defending-text-to-image-models-from#ran","syntology_url":"https://syntology.ai/paper/2403.01446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01446"}},"official":{"repos":["cure-lab/guardt2i"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/tree-regularized-tabular-embeddings","slug":"tree-regularized-tabular-embeddings","title":"Tree-Regularized Tabular Embeddings","date":"2024-03-01","arxiv_id":"2403.00963","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/tree-regularized-tabular-embeddings#ran","syntology_url":"https://syntology.ai/paper/2403.00963","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.00963"}},"official":{"repos":["milanlx/tree-regularized-embedding"],"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/the-kandy-benchmark-incremental-neuro","slug":"the-kandy-benchmark-incremental-neuro","title":"The KANDY Benchmark: Incremental Neuro-Symbolic Learning and Reasoning with Kandinsky Patterns","date":"2024-02-27","arxiv_id":"2402.17431","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":5,"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/the-kandy-benchmark-incremental-neuro#ran","syntology_url":"https://syntology.ai/paper/2402.17431","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.17431"}},"official":{"repos":["continual-nesy/kandybenchmark"],"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/self-consistent-conformal-prediction","slug":"self-consistent-conformal-prediction","title":"Self-Calibrating Conformal Prediction","date":"2024-02-11","arxiv_id":"2402.07307","repositories_listed":1,"syntology":{"n":25,"n_ran":23,"n_constructed":4,"n_ran_checked":9,"n_instrument":14,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"23 ran (of which 4 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 14 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/self-consistent-conformal-prediction#ran","syntology_url":"https://syntology.ai/paper/2402.07307","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.07307"}},"official":{"repos":["larsvanderlaan/selfcalibratingconformal"],"state":"official (archive's flag): 23 ran","n_ran":23,"n_constructed":4,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/bayesian-nonparametrics-meets-data-driven","slug":"bayesian-nonparametrics-meets-data-driven","title":"Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization","date":"2024-01-28","arxiv_id":"2401.15771","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bayesian-nonparametrics-meets-data-driven#ran","syntology_url":"https://syntology.ai/paper/2401.15771","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.15771"}},"official":{"repos":["nbariletto/bnp_for_dro"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/alleviating-structural-distribution-shift-in","slug":"alleviating-structural-distribution-shift-in","title":"Alleviating Structural Distribution Shift in Graph Anomaly Detection","date":"2024-01-25","arxiv_id":"2401.14155","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/alleviating-structural-distribution-shift-in#ran","syntology_url":"https://syntology.ai/paper/2401.14155","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.14155"}},"official":{"repos":["blacksingular/wsdm_gdn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-closer-look-at-auroc-and-auprc-under-class","slug":"a-closer-look-at-auroc-and-auprc-under-class","title":"A Closer Look at AUROC and AUPRC under Class Imbalance","date":"2024-01-11","arxiv_id":"2401.06091","repositories_listed":3,"syntology":{"n":34,"n_ran":25,"n_constructed":0,"n_ran_checked":25,"n_instrument":0,"n_unverified":9,"n_honours":1,"n_violates":1,"n_no_contract":23,"n_pointer_only":21,"phrase":"25 ran (of which 0 constructed an object rather than computing a result; 25 with no instrument failure: 1 honoured, 1 violated, 23 with no contract checked; 0 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/a-closer-look-at-auroc-and-auprc-under-class#ran","syntology_url":"https://syntology.ai/paper/2401.06091","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.06091"}},"official":{"repos":["lassehhansen/arxiv-search","hzhang0/auc_bias","mmcdermott/auc_is_all_you_need"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":7,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/benchmarking-distribution-shift-in-tabular-1","slug":"benchmarking-distribution-shift-in-tabular-1","title":"Benchmarking Distribution Shift in Tabular Data with TableShift","date":"2023-12-10","arxiv_id":"2312.07577","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/benchmarking-distribution-shift-in-tabular-1#ran","syntology_url":"https://syntology.ai/paper/2312.07577","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.07577"}},"official":{"repos":["mlfoundations/tableshift"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-brief-tutorial-on-sample-size-calculations","slug":"a-brief-tutorial-on-sample-size-calculations","title":"A Brief Tutorial on Sample Size Calculations for Fairness Audits","date":"2023-12-07","arxiv_id":"2312.04745","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":0,"n_no_contract":1,"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, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-brief-tutorial-on-sample-size-calculations#ran","syntology_url":"https://syntology.ai/paper/2312.04745","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.04745"}},"official":{"repos":["harvineet/sample-size-fairness-audits"],"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/a-comparison-between-invariant-and","slug":"a-comparison-between-invariant-and","title":"A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks","date":"2023-11-30","arxiv_id":"2311.18672","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/a-comparison-between-invariant-and#ran","syntology_url":"https://syntology.ai/paper/2311.18672","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.18672"}},"official":{"repos":["royforestano/2023_gsoc_ml4sci_qmlhep_gnn"],"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/learning-with-complementary-labels-revisited","slug":"learning-with-complementary-labels-revisited","title":"Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical","date":"2023-11-27","arxiv_id":"2311.15502","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":4,"n_instrument":5,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-with-complementary-labels-revisited#ran","syntology_url":"https://syntology.ai/paper/2311.15502","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.15502"}},"official":{"repos":["wwangwitsel/scarce"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/unified-classification-and-rejection-a-one","slug":"unified-classification-and-rejection-a-one","title":"Unified Classification and Rejection: A One-versus-All Framework","date":"2023-11-22","arxiv_id":"2311.13355","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unified-classification-and-rejection-a-one#ran","syntology_url":"https://syntology.ai/paper/2311.13355","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.13355"}},"official":{"repos":["zhen-cheng121/cpn_ova_unified"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/mmoe-mixture-of-multimodal-interaction","slug":"mmoe-mixture-of-multimodal-interaction","title":"MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction Experts","date":"2023-11-16","arxiv_id":"2311.09580","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/mmoe-mixture-of-multimodal-interaction#ran","syntology_url":"https://syntology.ai/paper/2311.09580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.09580"}},"official":{"repos":["lwaekfjlk/mmoe"],"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/19-parameters-is-all-you-need-tiny-neural","slug":"19-parameters-is-all-you-need-tiny-neural","title":"19 Parameters Is All You Need: Tiny Neural Networks for Particle Physics","date":"2023-10-24","arxiv_id":"2310.16121","repositories_listed":1,"syntology":{"n":17,"n_ran":17,"n_constructed":0,"n_ran_checked":13,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"17 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/19-parameters-is-all-you-need-tiny-neural#ran","syntology_url":"https://syntology.ai/paper/2310.16121","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.16121"}},"official":{"repos":["abogatskiy/pelican-nano"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/implicit-regularization-via-soft-ascent","slug":"implicit-regularization-via-soft-ascent","title":"Soft ascent-descent as a stable and flexible alternative to flooding","date":"2023-10-16","arxiv_id":"2310.10006","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":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/implicit-regularization-via-soft-ascent#ran","syntology_url":"https://syntology.ai/paper/2310.10006","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.10006"}},"official":{"repos":["feedbackward/bdd-flood"],"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/post-hoc-bias-scoring-is-optimal-for-fair","slug":"post-hoc-bias-scoring-is-optimal-for-fair","title":"Post-hoc Bias Scoring Is Optimal For Fair Classification","date":"2023-10-09","arxiv_id":"2310.05725","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":1,"n_instrument":6,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":7,"phrase":"7 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; 6 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/post-hoc-bias-scoring-is-optimal-for-fair#ran","syntology_url":"https://syntology.ai/paper/2310.05725","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.05725"}},"official":{"repos":["chenw20/biasscore"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/detecting-and-grounding-multi-modal-media-1","slug":"detecting-and-grounding-multi-modal-media-1","title":"Detecting and Grounding Multi-Modal Media Manipulation and Beyond","date":"2023-09-25","arxiv_id":"2309.14203","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":3,"n_honours":2,"n_violates":1,"n_no_contract":5,"n_pointer_only":14,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 1 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/detecting-and-grounding-multi-modal-media-1#ran","syntology_url":"https://syntology.ai/paper/2309.14203","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.14203"}},"official":{"repos":["rshaojimmy/multimodal-deepfake"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/detect-every-thing-with-few-examples","slug":"detect-every-thing-with-few-examples","title":"Detect Everything with Few Examples","date":"2023-09-22","arxiv_id":"2309.12969","repositories_listed":1,"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":1,"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/detect-every-thing-with-few-examples#ran","syntology_url":"https://syntology.ai/paper/2309.12969","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.12969"}},"official":{"repos":["mlzxy/devit"],"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/a-3d-explainability-framework-to-uncover","slug":"a-3d-explainability-framework-to-uncover","title":"An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognition","date":"2023-09-02","arxiv_id":"2309.00903","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/a-3d-explainability-framework-to-uncover#ran","syntology_url":"https://syntology.ai/paper/2309.00903","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.00903"}},"official":{"repos":["ece7048/3dsulci"],"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/vadclip-adapting-vision-language-models-for","slug":"vadclip-adapting-vision-language-models-for","title":"VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection","date":"2023-08-22","arxiv_id":"2308.11681","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":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/vadclip-adapting-vision-language-models-for#ran","syntology_url":"https://syntology.ai/paper/2308.11681","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.11681"}},"official":{"repos":["nwpu-zxr/vadclip"],"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/vi-net-boosting-category-level-6d-object-pose","slug":"vi-net-boosting-category-level-6d-object-pose","title":"VI-Net: Boosting Category-level 6D Object Pose Estimation via Learning Decoupled Rotations on the Spherical Representations","date":"2023-08-19","arxiv_id":"2308.09916","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/vi-net-boosting-category-level-6d-object-pose#ran","syntology_url":"https://syntology.ai/paper/2308.09916","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09916"}},"official":{"repos":["jiehonglin/vi-net"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dedode-detect-don-t-describe-describe-don-t","slug":"dedode-detect-don-t-describe-describe-don-t","title":"DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature Matching","date":"2023-08-16","arxiv_id":"2308.08479","repositories_listed":2,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/dedode-detect-don-t-describe-describe-don-t#ran","syntology_url":"https://syntology.ai/paper/2308.08479","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.08479"}},"official":{"repos":["parskatt/dedode"],"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/multi-label-knowledge-distillation","slug":"multi-label-knowledge-distillation","title":"Multi-Label Knowledge Distillation","date":"2023-08-12","arxiv_id":"2308.06453","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/multi-label-knowledge-distillation#ran","syntology_url":"https://syntology.ai/paper/2308.06453","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.06453"}},"official":{"repos":["penghui-yang/l2d"],"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/map-a-model-agnostic-pretraining-framework","slug":"map-a-model-agnostic-pretraining-framework","title":"MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction","date":"2023-08-03","arxiv_id":"2308.01737","repositories_listed":1,"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":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) · 0 unverified","sample_list":"/paper/map-a-model-agnostic-pretraining-framework#ran","syntology_url":"https://syntology.ai/paper/2308.01737","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.01737"}},"official":{"repos":["chiangel/map-code"],"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/described-object-detection-liberating-object-1","slug":"described-object-detection-liberating-object-1","title":"Described Object Detection: Liberating Object Detection with Flexible Expressions","date":"2023-07-24","arxiv_id":"2307.12813","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/described-object-detection-liberating-object-1#ran","syntology_url":"https://syntology.ai/paper/2307.12813","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.12813"}},"official":{"repos":["shikras/d-cube"],"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/a-benchmark-of-categorical-encoders-for-1","slug":"a-benchmark-of-categorical-encoders-for-1","title":"A benchmark of categorical encoders for binary classification","date":"2023-07-17","arxiv_id":"2307.09191","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-benchmark-of-categorical-encoders-for-1#ran","syntology_url":"https://syntology.ai/paper/2307.09191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.09191"}},"official":{"repos":["drcohomology/encoderbenchmarking"],"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/maintaining-plasticity-in-deep-continual","slug":"maintaining-plasticity-in-deep-continual","title":"Maintaining Plasticity in Deep Continual Learning","date":"2023-06-23","arxiv_id":"2306.13812","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":3,"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/maintaining-plasticity-in-deep-continual#ran","syntology_url":"https://syntology.ai/paper/2306.13812","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.13812"}},"official":{"repos":["shibhansh/loss-of-plasticity"],"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/agile3d-attention-guided-interactive-multi","slug":"agile3d-attention-guided-interactive-multi","title":"AGILE3D: Attention Guided Interactive Multi-object 3D Segmentation","date":"2023-06-01","arxiv_id":"2306.00977","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/agile3d-attention-guided-interactive-multi#ran","syntology_url":"https://syntology.ai/paper/2306.00977","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00977"}},"official":null}},{"url":"/paper/noisy-correspondence-learning-with-meta","slug":"noisy-correspondence-learning-with-meta","title":"Noisy Correspondence Learning with Meta Similarity Correction","date":"2023-04-13","arxiv_id":"2304.06275","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/noisy-correspondence-learning-with-meta#ran","syntology_url":"https://syntology.ai/paper/2304.06275","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.06275"}},"official":{"repos":["hhc1997/mscn"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/window-based-early-exit-cascades-for","slug":"window-based-early-exit-cascades-for","title":"Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Models","date":"2023-03-14","arxiv_id":"2303.08010","repositories_listed":1,"syntology":{"n":36,"n_ran":23,"n_constructed":0,"n_ran_checked":22,"n_instrument":1,"n_unverified":13,"n_honours":0,"n_violates":0,"n_no_contract":22,"n_pointer_only":0,"phrase":"23 ran (of which 0 constructed an object rather than computing a result; 22 with no instrument failure: 0 honoured, 0 violated, 22 with no contract checked; 1 where Syntology's instrument failed) · 13 unverified","sample_list":"/paper/window-based-early-exit-cascades-for#ran","syntology_url":"https://syntology.ai/paper/2303.08010","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.08010"}},"official":{"repos":["guoxoug/window-early-exit"],"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":["found_in_text","official"]}}},{"url":"/paper/learning-label-encodings-for-deep-regression","slug":"learning-label-encodings-for-deep-regression","title":"Learning Label Encodings for Deep Regression","date":"2023-03-04","arxiv_id":"2303.02273","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/learning-label-encodings-for-deep-regression#ran","syntology_url":"https://syntology.ai/paper/2303.02273","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.02273"}},"official":{"repos":["ubc-aamodt-group/rlel_regression"],"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/rs-del-edit-distance-robustness-certificates-1","slug":"rs-del-edit-distance-robustness-certificates-1","title":"RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion","date":"2023-01-31","arxiv_id":"2302.01757","repositories_listed":2,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":3,"n_honours":3,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 3 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/rs-del-edit-distance-robustness-certificates-1#ran","syntology_url":"https://syntology.ai/paper/2302.01757","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.01757"}},"official":{"repos":["dovermore/randomized-deletion"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/label-encoding-for-regression-networks-1","slug":"label-encoding-for-regression-networks-1","title":"Label Encoding for Regression Networks","date":"2022-12-04","arxiv_id":"2212.01927","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/label-encoding-for-regression-networks-1#ran","syntology_url":"https://syntology.ai/paper/2212.01927","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.01927"}},"official":{"repos":["ubc-aamodt-group/bel_regression"],"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/two-is-better-than-many-binary-classification","slug":"two-is-better-than-many-binary-classification","title":"Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering","date":"2022-10-29","arxiv_id":"2210.16495","repositories_listed":1,"syntology":{"n":7,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/two-is-better-than-many-binary-classification#ran","syntology_url":"https://syntology.ai/paper/2210.16495","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.16495"}},"official":{"repos":["declare-lab/team"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/margin-optimal-classification-trees","slug":"margin-optimal-classification-trees","title":"Margin Optimal Classification Trees","date":"2022-10-19","arxiv_id":"2210.10567","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/margin-optimal-classification-trees#ran","syntology_url":"https://syntology.ai/paper/2210.10567","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.10567"}},"official":{"repos":["m-monaci/MARGOT"],"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/contrastive-neural-ratio-estimation","slug":"contrastive-neural-ratio-estimation","title":"Contrastive Neural Ratio Estimation for Simulation-based Inference","date":"2022-10-11","arxiv_id":"2210.06170","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/contrastive-neural-ratio-estimation#ran","syntology_url":"https://syntology.ai/paper/2210.06170","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06170"}},"official":{"repos":["bkmi/cnre"],"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/temporal-label-smoothing-for-early-prediction","slug":"temporal-label-smoothing-for-early-prediction","title":"Temporal Label Smoothing for Early Event Prediction","date":"2022-08-29","arxiv_id":"2208.13764","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/temporal-label-smoothing-for-early-prediction#ran","syntology_url":"https://syntology.ai/paper/2208.13764","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.13764"}},"official":{"repos":["ratschlab/tls"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-vision-transformers-for","slug":"self-supervised-vision-transformers-for","title":"Self-Supervised Vision Transformers for Malware Detection","date":"2022-08-15","arxiv_id":"2208.07049","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":4,"phrase":"8 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-supervised-vision-transformers-for#ran","syntology_url":"https://syntology.ai/paper/2208.07049","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.07049"}},"official":{"repos":["sachith500/sherlock"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/realpatch-a-statistical-matching-framework","slug":"realpatch-a-statistical-matching-framework","title":"RealPatch: A Statistical Matching Framework for Model Patching with Real Samples","date":"2022-08-03","arxiv_id":"2208.02192","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/realpatch-a-statistical-matching-framework#ran","syntology_url":"https://syntology.ai/paper/2208.02192","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.02192"}},"official":{"repos":["wearepal/realpatch"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-usefulness-of-deep-ensemble-diversity","slug":"on-the-usefulness-of-deep-ensemble-diversity","title":"On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution Detection","date":"2022-07-15","arxiv_id":"2207.07517","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/on-the-usefulness-of-deep-ensemble-diversity#ran","syntology_url":"https://syntology.ai/paper/2207.07517","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.07517"}},"official":{"repos":["guoxoug/ens-div-ood-detect"],"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/fair-generalized-linear-models-with-a-convex","slug":"fair-generalized-linear-models-with-a-convex","title":"Fair Generalized Linear Models with a Convex Penalty","date":"2022-06-18","arxiv_id":"2206.09076","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fair-generalized-linear-models-with-a-convex#ran","syntology_url":"https://syntology.ai/paper/2206.09076","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.09076"}},"official":{"repos":["hyungrok-do/fair-glm-cvx"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/undersampling-is-a-minimax-optimal-robustness","slug":"undersampling-is-a-minimax-optimal-robustness","title":"Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification","date":"2022-05-26","arxiv_id":"2205.13094","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/undersampling-is-a-minimax-optimal-robustness#ran","syntology_url":"https://syntology.ai/paper/2205.13094","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13094"}},"official":{"repos":["niladri-chatterji/undersampling-minimax"],"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/contrastive-representation-learning-for-cross","slug":"contrastive-representation-learning-for-cross","title":"Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities","date":"2022-05-23","arxiv_id":"2205.11438","repositories_listed":0,"syntology":{"n":18,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":8,"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) · 8 unverified","sample_list":"/paper/contrastive-representation-learning-for-cross#ran","syntology_url":"https://syntology.ai/paper/2205.11438","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.11438"}},"official":null}},{"url":"/paper/the-multimarginal-optimal-transport","slug":"the-multimarginal-optimal-transport","title":"The Multimarginal Optimal Transport Formulation of Adversarial Multiclass Classification","date":"2022-04-27","arxiv_id":"2204.12676","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":1,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-multimarginal-optimal-transport#ran","syntology_url":"https://syntology.ai/paper/2204.12676","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.12676"}},"official":null}},{"url":"/paper/masked-discrimination-for-self-supervised","slug":"masked-discrimination-for-self-supervised","title":"Masked Discrimination for Self-Supervised Learning on Point Clouds","date":"2022-03-21","arxiv_id":"2203.11183","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/masked-discrimination-for-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2203.11183","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11183"}},"official":{"repos":["haotian-liu/maskpoint"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/summarizing-differences-between-text","slug":"summarizing-differences-between-text","title":"Describing Differences between Text Distributions with Natural Language","date":"2022-01-28","arxiv_id":"2201.12323","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 0 unverified","sample_list":"/paper/summarizing-differences-between-text#ran","syntology_url":"https://syntology.ai/paper/2201.12323","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.12323"}},"official":{"repos":["ruiqi-zhong/describedistributionaldifferences"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/to-smote-or-not-to-smote","slug":"to-smote-or-not-to-smote","title":"To SMOTE, or not to SMOTE?","date":"2022-01-21","arxiv_id":"2201.08528","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/to-smote-or-not-to-smote#ran","syntology_url":"https://syntology.ai/paper/2201.08528","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.08528"}},"official":{"repos":["aws/to-smote-or-not"],"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/learning-class-prototypes-from-synthetic","slug":"learning-class-prototypes-from-synthetic","title":"Learning from Synthetic InSAR with Vision Transformers: The case of volcanic unrest detection","date":"2022-01-09","arxiv_id":"2201.03016","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-class-prototypes-from-synthetic#ran","syntology_url":"https://syntology.ai/paper/2201.03016","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.03016"}},"official":{"repos":["orion-ai-lab/prototypeinsar"],"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/inverse-weighted-survival-games","slug":"inverse-weighted-survival-games","title":"Inverse-Weighted Survival Games","date":"2021-11-16","arxiv_id":"2111.08175","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":4,"n_instrument":5,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":2,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 1 violated, 2 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/inverse-weighted-survival-games#ran","syntology_url":"https://syntology.ai/paper/2111.08175","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.08175"}},"official":{"repos":["rajesh-lab/inverse-weighted-survival-games"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/benchmarking-multimodal-automl-for-tabular","slug":"benchmarking-multimodal-automl-for-tabular","title":"Benchmarking Multimodal AutoML for Tabular Data with Text Fields","date":"2021-11-04","arxiv_id":"2111.02705","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/benchmarking-multimodal-automl-for-tabular#ran","syntology_url":"https://syntology.ai/paper/2111.02705","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.02705"}},"official":{"repos":["sxjscience/automl_multimodal_benchmark","awslabs/autogluon"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/implicit-bias-of-linear-equivariant-networks-1","slug":"implicit-bias-of-linear-equivariant-networks-1","title":"Implicit Bias of Linear Equivariant Networks","date":"2021-10-12","arxiv_id":"2110.06084","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/implicit-bias-of-linear-equivariant-networks-1#ran","syntology_url":"https://syntology.ai/paper/2110.06084","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.06084"}},"official":{"repos":["kristian-georgiev/implicit-bias-of-linear-equivariant-networks"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ucsl-a-machine-learning-expectation","slug":"ucsl-a-machine-learning-expectation","title":"UCSL : A Machine Learning Expectation-Maximization framework for Unsupervised Clustering driven by Supervised Learning","date":"2021-07-05","arxiv_id":"2107.01988","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/ucsl-a-machine-learning-expectation#ran","syntology_url":"https://syntology.ai/paper/2107.01988","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.01988"}},"official":{"repos":["neurospin-projects/2021_rlouiset_ucsl"],"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/visitron-visual-semantics-aligned","slug":"visitron-visual-semantics-aligned","title":"VISITRON: Visual Semantics-Aligned Interactively Trained Object-Navigator","date":"2021-05-25","arxiv_id":"2105.11589","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/visitron-visual-semantics-aligned#ran","syntology_url":"https://syntology.ai/paper/2105.11589","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.11589"}},"official":{"repos":["alexa/visitron"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pairwise-fairness-for-ordinal-regression","slug":"pairwise-fairness-for-ordinal-regression","title":"Pairwise Fairness for Ordinal Regression","date":"2021-05-07","arxiv_id":"2105.03153","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pairwise-fairness-for-ordinal-regression#ran","syntology_url":"https://syntology.ai/paper/2105.03153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.03153"}},"official":{"repos":["amazon-research/fair-ordinal-regression"],"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/benchmarking-representation-learning-for","slug":"benchmarking-representation-learning-for","title":"Benchmarking Representation Learning for Natural World Image Collections","date":"2021-03-30","arxiv_id":"2103.16483","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":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) · 5 unverified","sample_list":"/paper/benchmarking-representation-learning-for#ran","syntology_url":"https://syntology.ai/paper/2103.16483","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16483"}},"official":{"repos":["visipedia/newt"],"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/fbcnet-a-multi-view-convolutional-neural","slug":"fbcnet-a-multi-view-convolutional-neural","title":"FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface","date":"2021-03-17","arxiv_id":"2104.01233","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/fbcnet-a-multi-view-convolutional-neural#ran","syntology_url":"https://syntology.ai/paper/2104.01233","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.01233"}},"official":{"repos":["ravikiran-mane/FBCNet"],"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/multi-attentional-deepfake-detection","slug":"multi-attentional-deepfake-detection","title":"Multi-attentional Deepfake Detection","date":"2021-03-03","arxiv_id":"2103.02406","repositories_listed":1,"syntology":{"n":25,"n_ran":20,"n_constructed":10,"n_ran_checked":17,"n_instrument":3,"n_unverified":5,"n_honours":3,"n_violates":0,"n_no_contract":14,"n_pointer_only":25,"phrase":"20 ran (of which 10 constructed an object rather than computing a result; 17 with no instrument failure: 3 honoured, 0 violated, 14 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/multi-attentional-deepfake-detection#ran","syntology_url":"https://syntology.ai/paper/2103.02406","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.02406"}},"official":{"repos":["yoctta/multiple-attention"],"state":"official (archive's flag): 20 ran","n_ran":20,"n_constructed":10,"n_ran_no_instrument_failure":17,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/fermi-fair-empirical-risk-minimization-via","slug":"fermi-fair-empirical-risk-minimization-via","title":"A Stochastic Optimization Framework for Fair Risk Minimization","date":"2021-02-24","arxiv_id":"2102.12586","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/fermi-fair-empirical-risk-minimization-via#ran","syntology_url":"https://syntology.ai/paper/2102.12586","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.12586"}},"official":{"repos":["optimization-for-data-driven-science/FERMI"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/constrained-optimization-for-training-deep","slug":"constrained-optimization-for-training-deep","title":"Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes","date":"2021-02-21","arxiv_id":"2102.12894","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/constrained-optimization-for-training-deep#ran","syntology_url":"https://syntology.ai/paper/2102.12894","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.12894"}},"official":{"repos":["salusanga/alm-dnn"],"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/how-faithful-is-your-synthetic-data-sample","slug":"how-faithful-is-your-synthetic-data-sample","title":"How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models","date":"2021-02-17","arxiv_id":"2102.08921","repositories_listed":4,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 0 unverified","sample_list":"/paper/how-faithful-is-your-synthetic-data-sample#ran","syntology_url":"https://syntology.ai/paper/2102.08921","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.08921"}},"official":{"repos":["vanderschaarlab/evaluating-generative-models"],"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":["listed","official"]}}},{"url":"/paper/on-using-classification-datasets-to-evaluate","slug":"on-using-classification-datasets-to-evaluate","title":"On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insights","date":"2020-12-23","arxiv_id":"2012.12931","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/on-using-classification-datasets-to-evaluate#ran","syntology_url":"https://syntology.ai/paper/2012.12931","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.12931"}},"official":{"repos":["LingxiaoShawn/GLOD-Issues"],"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-bayesian-classification-using-an","slug":"robust-bayesian-classification-using-an","title":"Robust Bayesian Classification Using an Optimistic Score Ratio","date":"2020-07-08","arxiv_id":"2007.04458","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/robust-bayesian-classification-using-an#ran","syntology_url":"https://syntology.ai/paper/2007.04458","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.04458"}},"official":{"repos":["nian-si/bsc"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptive-gradient-methods-converge-faster","slug":"adaptive-gradient-methods-converge-faster","title":"Adaptive Gradient Methods Converge Faster with Over-Parameterization (but you should do a line-search)","date":"2020-06-11","arxiv_id":"2006.06835","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adaptive-gradient-methods-converge-faster#ran","syntology_url":"https://syntology.ai/paper/2006.06835","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.06835"}},"official":null}},{"url":"/paper/mc-bert-efficient-language-pre-training-via-a","slug":"mc-bert-efficient-language-pre-training-via-a","title":"MC-BERT: Efficient Language Pre-Training via a Meta Controller","date":"2020-06-10","arxiv_id":"2006.05744","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mc-bert-efficient-language-pre-training-via-a#ran","syntology_url":"https://syntology.ai/paper/2006.05744","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.05744"}},"official":{"repos":["MC-BERT/MC-BERT"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/the-hateful-memes-challenge-detecting-hate","slug":"the-hateful-memes-challenge-detecting-hate","title":"The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes","date":"2020-05-10","arxiv_id":"2005.04790","repositories_listed":4,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/the-hateful-memes-challenge-detecting-hate#ran","syntology_url":"https://syntology.ai/paper/2005.04790","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.04790"}},"official":{"repos":["facebookresearch/mmf"],"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/learning-from-aggregate-observations","slug":"learning-from-aggregate-observations","title":"Learning from Aggregate Observations","date":"2020-04-14","arxiv_id":"2004.06316","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":2,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"6 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-from-aggregate-observations#ran","syntology_url":"https://syntology.ai/paper/2004.06316","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.06316"}},"official":{"repos":["YivanZhang/lio"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/topology-of-deep-neural-networks","slug":"topology-of-deep-neural-networks","title":"Topology of deep neural networks","date":"2020-04-13","arxiv_id":"2004.06093","repositories_listed":1,"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":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) · 0 unverified","sample_list":"/paper/topology-of-deep-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2004.06093","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.06093"}},"official":null}},{"url":"/paper/towards-a-predictive-spatio-temporal","slug":"towards-a-predictive-spatio-temporal","title":"Towards a predictive spatio-temporal representation of brain data","date":"2020-02-29","arxiv_id":"2003.03290","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-a-predictive-spatio-temporal#ran","syntology_url":"https://syntology.ai/paper/2003.03290","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.03290"}},"official":null}},{"url":"/paper/efficient-policy-learning-from-surrogate-loss","slug":"efficient-policy-learning-from-surrogate-loss","title":"Efficient Policy Learning from Surrogate-Loss Classification Reductions","date":"2020-02-12","arxiv_id":"2002.05153","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-policy-learning-from-surrogate-loss#ran","syntology_url":"https://syntology.ai/paper/2002.05153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.05153"}},"official":{"repos":["CausalML/ESPRM"],"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/beyond-temperature-scaling-obtaining-well","slug":"beyond-temperature-scaling-obtaining-well","title":"Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration","date":"2019-10-28","arxiv_id":"1910.12656","repositories_listed":3,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/beyond-temperature-scaling-obtaining-well#ran","syntology_url":"https://syntology.ai/paper/1910.12656","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.12656"}},"official":null}},{"url":"/paper/the-implicit-bias-of-depth-how-incremental","slug":"the-implicit-bias-of-depth-how-incremental","title":"The Implicit Bias of Depth: How Incremental Learning Drives Generalization","date":"2019-09-26","arxiv_id":"1909.12051","repositories_listed":1,"syntology":{"n":9,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/the-implicit-bias-of-depth-how-incremental#ran","syntology_url":"https://syntology.ai/paper/1909.12051","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.12051"}},"official":{"repos":["dsgissin/Incremental-Learning"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/imbalance-xgboost-leveraging-weighted-and","slug":"imbalance-xgboost-leveraging-weighted-and","title":"Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost","date":"2019-08-05","arxiv_id":"1908.01672","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":0,"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/imbalance-xgboost-leveraging-weighted-and#ran","syntology_url":"https://syntology.ai/paper/1908.01672","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.01672"}},"official":{"repos":["jhwjhw0123/Imbalance-XGBoost"],"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/anti-money-laundering-in-bitcoin","slug":"anti-money-laundering-in-bitcoin","title":"Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics","date":"2019-07-31","arxiv_id":"1908.02591","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/anti-money-laundering-in-bitcoin#ran","syntology_url":"https://syntology.ai/paper/1908.02591","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.02591"}},"official":null}},{"url":"/paper/characterization-of-overlap-in-observational","slug":"characterization-of-overlap-in-observational","title":"Characterization of Overlap in Observational Studies","date":"2019-07-09","arxiv_id":"1907.04138","repositories_listed":1,"syntology":{"n":13,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":6,"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) · 6 unverified","sample_list":"/paper/characterization-of-overlap-in-observational#ran","syntology_url":"https://syntology.ai/paper/1907.04138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.04138"}},"official":{"repos":["clinicalml/overlap-code"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["official"]}}}],"record_sha256":"fa07035ab785193bf38b93d671a3635742855c4b55557a41b188b2209519efd2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}