{"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/multi-label-classification/papers/4","list_of":"/task/multi-label-classification","task":"Multi-Label 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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":12,"rows_per_page":100,"rows":[301,400],"of":1198,"counts":{"archive_papers_tagged":1198,"with_a_code_link":459,"where_syntology_ran_a_sample":75,"not_listed_spam_title":0,"listed":1198,"listed_where_code_ran":75,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":57,"every_run_a_failure_of_syntologys_instrument":18,"listed_with_a_run_with_no_instrument_failure":57,"listed_every_run_a_failure_of_syntologys_instrument":18,"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/multi-label-classification","prev":"/task/multi-label-classification/papers/3","next":"/task/multi-label-classification/papers/5","papers":[{"url":"/paper/tribuo-machine-learning-with-provenance-in","slug":"tribuo-machine-learning-with-provenance-in","title":"Tribuo: Machine Learning with Provenance in Java","date":"2021-10-06","arxiv_id":"2110.03022","repositories_listed":1,"syntology":null},{"url":"/paper/contractnli-a-dataset-for-document-level","slug":"contractnli-a-dataset-for-document-level","title":"ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts","date":"2021-10-05","arxiv_id":"2110.01799","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"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) · 1 unverified","sample_list":"/paper/contractnli-a-dataset-for-document-level#ran","syntology_url":"https://syntology.ai/paper/2110.01799","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.01799"}},"official":{"repos":["stanfordnlp/contract-nli-bert"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/sound-event-detection-transformer-an-event","slug":"sound-event-detection-transformer-an-event","title":"Sound Event Detection Transformer: An Event-based End-to-End Model for Sound Event Detection","date":"2021-10-05","arxiv_id":"2110.02011","repositories_listed":1,"syntology":null},{"url":"/paper/lexglue-a-benchmark-dataset-for-legal","slug":"lexglue-a-benchmark-dataset-for-legal","title":"LexGLUE: A Benchmark Dataset for Legal Language Understanding in English","date":"2021-10-03","arxiv_id":"2110.00976","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/lexglue-a-benchmark-dataset-for-legal#ran","syntology_url":"https://syntology.ai/paper/2110.00976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.00976"}},"official":{"repos":["coastalcph/lex-glue"],"state":"official: not harvested","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"url":"/paper/m3tr-multi-modal-multi-label-recognition-with","slug":"m3tr-multi-modal-multi-label-recognition-with","title":"M3TR: Multi-modal Multi-label Recognition with Transformer","date":"2021-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/predicting-covid-19-patient-shielding-a","slug":"predicting-covid-19-patient-shielding-a","title":"Predicting COVID-19 Patient Shielding: A Comprehensive Study","date":"2021-10-01","arxiv_id":"2110.00183","repositories_listed":1,"syntology":null},{"url":"/paper/can-multi-label-classification-networks-know","slug":"can-multi-label-classification-networks-know","title":"Can multi-label classification networks know what they don't know?","date":"2021-09-29","arxiv_id":"2109.14162","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/can-multi-label-classification-networks-know#ran","syntology_url":"https://syntology.ai/paper/2109.14162","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.14162"}},"official":{"repos":["deeplearning-wisc/multi-label-ood"],"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/modeling-label-space-interactions-in-multi","slug":"modeling-label-space-interactions-in-multi","title":"Modeling Label Space Interactions in Multi-label Classification using Box Embeddings","date":"2021-09-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/jointly-modeling-aspect-and-polarity-for","slug":"jointly-modeling-aspect-and-polarity-for","title":"Jointly Modeling Aspect and Polarity for Aspect-based Sentiment Analysis in Persian Reviews","date":"2021-09-16","arxiv_id":"2109.07680","repositories_listed":1,"syntology":null},{"url":"/paper/learning-with-holographic-reduced","slug":"learning-with-holographic-reduced","title":"Learning with Holographic Reduced Representations","date":"2021-09-05","arxiv_id":"2109.02157","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/learning-with-holographic-reduced#ran","syntology_url":"https://syntology.ai/paper/2109.02157","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.02157"}},"official":{"repos":["NeuromorphicComputationResearchProgram/Learning-with-Holographic-Reduced-Representations"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/aimh-at-semeval-2021-task-6-multimodal","slug":"aimh-at-semeval-2021-task-6-multimodal","title":"AIMH at SemEval-2021 Task 6: Multimodal Classification Using an Ensemble of Transformer Models","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/decaf-deep-extreme-classification-with-label","slug":"decaf-deep-extreme-classification-with-label","title":"DECAF: Deep Extreme Classification with Label Features","date":"2021-08-01","arxiv_id":"2108.00368","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/decaf-deep-extreme-classification-with-label#ran","syntology_url":"https://syntology.ai/paper/2108.00368","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.00368"}},"official":{"repos":["Extreme-classification/DECAF"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/eclare-extreme-classification-with-label","slug":"eclare-extreme-classification-with-label","title":"ECLARE: Extreme Classification with Label Graph Correlations","date":"2021-07-31","arxiv_id":"2108.00261","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/eclare-extreme-classification-with-label#ran","syntology_url":"https://syntology.ai/paper/2108.00261","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.00261"}},"official":{"repos":["Extreme-classification/ECLARE"],"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/attack-transferability-characterization-for","slug":"attack-transferability-characterization-for","title":"Attack Transferability Characterization for Adversarially Robust Multi-label Classification","date":"2021-06-29","arxiv_id":"2106.15360","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-diagnostic-label-correlation-for-1","slug":"modeling-diagnostic-label-correlation-for-1","title":"Modeling Diagnostic Label Correlation for Automatic ICD Coding","date":"2021-06-24","arxiv_id":"2106.12800","repositories_listed":1,"syntology":null},{"url":"/paper/extreme-multi-label-learning-for-semantic","slug":"extreme-multi-label-learning-for-semantic","title":"Extreme Multi-label Learning for Semantic Matching in Product Search","date":"2021-06-23","arxiv_id":"2106.12657","repositories_listed":1,"syntology":null},{"url":"/paper/multi-layered-semantic-representation-network","slug":"multi-layered-semantic-representation-network","title":"Multi-layered Semantic Representation Network for Multi-label Image Classification","date":"2021-06-22","arxiv_id":"2106.11596","repositories_listed":1,"syntology":null},{"url":"/paper/amalgamating-knowledge-from-heterogeneous","slug":"amalgamating-knowledge-from-heterogeneous","title":"Amalgamating Knowledge From Heterogeneous Graph Neural Networks","date":"2021-06-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-person-re-identification-via-3","slug":"unsupervised-person-re-identification-via-3","title":"Unsupervised Person Re-identification via Multi-Label Prediction and Classification based on Graph-Structural Insight","date":"2021-06-16","arxiv_id":"2106.08798","repositories_listed":1,"syntology":null},{"url":"/paper/mltr-multi-label-classification-with","slug":"mltr-multi-label-classification-with","title":"MlTr: Multi-label Classification with Transformer","date":"2021-06-11","arxiv_id":"2106.06195","repositories_listed":1,"syntology":null},{"url":"/paper/multiprover-generating-multiple-proofs-for","slug":"multiprover-generating-multiple-proofs-for","title":"multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning","date":"2021-06-02","arxiv_id":"2106.01354","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/multiprover-generating-multiple-proofs-for#ran","syntology_url":"https://syntology.ai/paper/2106.01354","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.01354"}},"official":{"repos":["swarnaHub/multiPRover"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/esc-redesigning-wsd-with-extractive-sense","slug":"esc-redesigning-wsd-with-extractive-sense","title":"ESC: Redesigning WSD with Extractive Sense Comprehension","date":"2021-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/volta-at-semeval-2021-task-6-towards","slug":"volta-at-semeval-2021-task-6-towards","title":"Volta at SemEval-2021 Task 6: Towards Detecting Persuasive Texts and Images using Textual and Multimodal Ensemble","date":"2021-06-01","arxiv_id":"2106.00240","repositories_listed":1,"syntology":null},{"url":"/paper/prsl-interpretable-multi-label-stacking-by","slug":"prsl-interpretable-multi-label-stacking-by","title":"pRSL: Interpretable Multi-label Stacking by Learning Probabilistic Rules","date":"2021-05-28","arxiv_id":"2105.13850","repositories_listed":1,"syntology":null},{"url":"/paper/can-multi-label-classification-networks-know-1","slug":"can-multi-label-classification-networks-know-1","title":"Can multi-label classification networks know what they don’t know?","date":"2021-05-21","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-uncertainty-aware-collaborative","slug":"a-novel-uncertainty-aware-collaborative","title":"A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-Labels","date":"2021-05-12","arxiv_id":"2105.05496","repositories_listed":1,"syntology":null},{"url":"/paper/priberam-at-mesinesp-multi-label","slug":"priberam-at-mesinesp-multi-label","title":"Priberam at MESINESP Multi-label Classification of Medical Texts Task","date":"2021-05-12","arxiv_id":"2105.05614","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-diversity-learning-for-zero-shot","slug":"semantic-diversity-learning-for-zero-shot","title":"Semantic Diversity Learning for Zero-Shot Multi-label Classification","date":"2021-05-12","arxiv_id":"2105.05926","repositories_listed":1,"syntology":null},{"url":"/paper/interpretation-of-multi-label-classification","slug":"interpretation-of-multi-label-classification","title":"Interpretation of multi-label classification models using shapley values","date":"2021-04-21","arxiv_id":"2104.10505","repositories_listed":1,"syntology":null},{"url":"/paper/whose-heritage-classification-of-unesco-world","slug":"whose-heritage-classification-of-unesco-world","title":"WHOSe Heritage: Classification of UNESCO World Heritage \"Outstanding Universal Value\" Documents with Soft Labels","date":"2021-04-12","arxiv_id":"2104.05547","repositories_listed":1,"syntology":null},{"url":"/paper/choice-aware-user-engagement-modeling","slug":"choice-aware-user-engagement-modeling","title":"Choice-Aware User Engagement Modeling andOptimization on Social Media","date":"2021-04-01","arxiv_id":"2104.00801","repositories_listed":1,"syntology":null},{"url":"/paper/framing-word-sense-disambiguation-as-a-multi","slug":"framing-word-sense-disambiguation-as-a-multi","title":"Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration","date":"2021-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-classification-neural-networks","slug":"multi-label-classification-neural-networks","title":"Multi-Label Classification Neural Networks with Hard Logical Constraints","date":"2021-03-24","arxiv_id":"2103.13427","repositories_listed":1,"syntology":null},{"url":"/paper/sewer-ml-a-multi-label-sewer-defect","slug":"sewer-ml-a-multi-label-sewer-defect","title":"Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark","date":"2021-03-19","arxiv_id":"2103.10895","repositories_listed":1,"syntology":null},{"url":"/paper/noisy-label-learning-for-large-scale-medical","slug":"noisy-label-learning-for-large-scale-medical","title":"NVUM: Non-Volatile Unbiased Memory for Robust Medical Image Classification","date":"2021-03-06","arxiv_id":"2103.04053","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-mean-teacher-for-semi","slug":"self-supervised-mean-teacher-for-semi","title":"Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification","date":"2021-03-05","arxiv_id":"2103.03629","repositories_listed":1,"syntology":null},{"url":"/paper/towards-evaluating-the-robustness-of-deep","slug":"towards-evaluating-the-robustness-of-deep","title":"Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack","date":"2021-03-05","arxiv_id":"2103.03438","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-multi-label-action-dependencies-for","slug":"modeling-multi-label-action-dependencies-for","title":"Modeling Multi-Label Action Dependencies for Temporal Action Localization","date":"2021-03-04","arxiv_id":"2103.03027","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":3,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"5 ran (of which 3 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/modeling-multi-label-action-dependencies-for#ran","syntology_url":"https://syntology.ai/paper/2103.03027","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.03027"}},"official":{"repos":["ptirupat/MLAD"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-graph-convolutional-networks-for","slug":"learning-graph-convolutional-networks-for","title":"Learning Graph Convolutional Networks for Multi-Label Recognition and Applications","date":"2021-03-03","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-classification-via-adaptive","slug":"multi-label-classification-via-adaptive","title":"Multi-label Classification via Adaptive Resonance Theory-based Clustering","date":"2021-03-02","arxiv_id":"2103.01511","repositories_listed":1,"syntology":null},{"url":"/paper/covid-19-tweets-analysis-through-transformer","slug":"covid-19-tweets-analysis-through-transformer","title":"COVID-19 Tweets Analysis through Transformer Language Models","date":"2021-02-27","arxiv_id":"2103.00199","repositories_listed":1,"syntology":null},{"url":"/paper/top-k-extreme-contextual-bandits-with-arm","slug":"top-k-extreme-contextual-bandits-with-arm","title":"Top-$k$ eXtreme Contextual Bandits with Arm Hierarchy","date":"2021-02-15","arxiv_id":"2102.07800","repositories_listed":1,"syntology":null},{"url":"/paper/reliefe-feature-ranking-in-high-dimensional","slug":"reliefe-feature-ranking-in-high-dimensional","title":"ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings","date":"2021-01-23","arxiv_id":"2101.09577","repositories_listed":1,"syntology":null},{"url":"/paper/a-shallow-neural-model-for-relation","slug":"a-shallow-neural-model-for-relation","title":"A shallow neural model for relation prediction","date":"2021-01-22","arxiv_id":"2101.09090","repositories_listed":1,"syntology":null},{"url":"/paper/joint-learning-of-hyperbolic-label-embeddings","slug":"joint-learning-of-hyperbolic-label-embeddings","title":"Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification","date":"2021-01-13","arxiv_id":"2101.04997","repositories_listed":1,"syntology":null},{"url":"/paper/pdan-pyramid-dilated-attention-network-for","slug":"pdan-pyramid-dilated-attention-network-for","title":"PDAN: Pyramid Dilated Attention Network for Action Detection","date":"2021-01-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ccml-a-novel-collaborative-learning-model-for","slug":"ccml-a-novel-collaborative-learning-model-for","title":"Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification","date":"2020-12-19","arxiv_id":"2012.10715","repositories_listed":1,"syntology":null},{"url":"/paper/attention-driven-dynamic-graph-convolutional-1","slug":"attention-driven-dynamic-graph-convolutional-1","title":"Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition","date":"2020-12-05","arxiv_id":"2012.02994","repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-contrastive-learning-for-abstract","slug":"multi-label-contrastive-learning-for-abstract","title":"Multi-Label Contrastive Learning for Abstract Visual Reasoning","date":"2020-12-03","arxiv_id":"2012.01944","repositories_listed":1,"syntology":null},{"url":"/paper/retrieving-skills-from-job-descriptions-a","slug":"retrieving-skills-from-job-descriptions-a","title":"Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-multi-task-learning-for-multi","slug":"semi-supervised-multi-task-learning-for-multi","title":"Semi-supervised Multi-task Learning for Multi-label Fine-grained Sexism Classification","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/topic-modelling-brazilian-supreme-court","slug":"topic-modelling-brazilian-supreme-court","title":"Topic Modelling Brazilian Supreme Court Lawsuits","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-classification-do-hamming-loss-1","slug":"multi-label-classification-do-hamming-loss-1","title":"Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?","date":"2020-11-16","arxiv_id":"2011.07805","repositories_listed":1,"syntology":null},{"url":"/paper/nlpgym-a-toolkit-for-evaluating-rl-agents-on","slug":"nlpgym-a-toolkit-for-evaluating-rl-agents-on","title":"NLPGym -- A toolkit for evaluating RL agents on Natural Language Processing Tasks","date":"2020-11-16","arxiv_id":"2011.08272","repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-classification-using-link","slug":"multi-label-classification-using-link","title":"Multi-Label Classification Using Link Prediction","date":"2020-11-11","arxiv_id":"2011.05476","repositories_listed":1,"syntology":null},{"url":"/paper/document-level-relation-extraction-with","slug":"document-level-relation-extraction-with","title":"Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling","date":"2020-10-21","arxiv_id":"2010.11304","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/document-level-relation-extraction-with#ran","syntology_url":"https://syntology.ai/paper/2010.11304","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.11304"}},"official":{"repos":["wzhouad/ATLOP"],"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/coherent-hierarchical-multi-label","slug":"coherent-hierarchical-multi-label","title":"Coherent Hierarchical Multi-Label Classification Networks","date":"2020-10-20","arxiv_id":"2010.10151","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-deep-learning-for-automatic","slug":"interpretable-deep-learning-for-automatic","title":"Interpretable Deep Learning for Automatic Diagnosis of 12-lead Electrocardiogram","date":"2020-10-20","arxiv_id":"2010.10328","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-build-user-tag-profile-in","slug":"learning-to-build-user-tag-profile-in","title":"Learning to Build User-tag Profile in Recommendation System (UTPM)","date":"2020-10-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-few-zero-shot-learning-with","slug":"multi-label-few-zero-shot-learning-with","title":"Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs","date":"2020-10-15","arxiv_id":"2010.07459","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/multi-label-few-zero-shot-learning-with#ran","syntology_url":"https://syntology.ai/paper/2010.07459","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.07459"}},"official":{"repos":["MemoriesJ/KAMG"],"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/toxic-language-detection-in-social-media-for","slug":"toxic-language-detection-in-social-media-for","title":"Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis","date":"2020-10-09","arxiv_id":"2010.04543","repositories_listed":1,"syntology":null},{"url":"/paper/a-fully-hyperbolic-neural-model-for","slug":"a-fully-hyperbolic-neural-model-for","title":"A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification","date":"2020-10-05","arxiv_id":"2010.02053","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/a-fully-hyperbolic-neural-model-for#ran","syntology_url":"https://syntology.ai/paper/2010.02053","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.02053"}},"official":{"repos":["nlpAThits/hyfi"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/an-empirical-study-on-large-scale-multi-label","slug":"an-empirical-study-on-large-scale-multi-label","title":"An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels","date":"2020-10-04","arxiv_id":"2010.01653","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"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) · 1 unverified","sample_list":"/paper/an-empirical-study-on-large-scale-multi-label#ran","syntology_url":"https://syntology.ai/paper/2010.01653","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.01653"}},"official":{"repos":["iliaschalkidis/lmtc-eurlex57k"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-supervised-learning-and","slug":"rethinking-supervised-learning-and","title":"Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems","date":"2020-09-21","arxiv_id":"2009.09781","repositories_listed":1,"syntology":null},{"url":"/paper/neural-networks-enhancement-through-prior","slug":"neural-networks-enhancement-through-prior","title":"Neural Networks Enhancement with Logical Knowledge","date":"2020-09-13","arxiv_id":"2009.06087","repositories_listed":1,"syntology":null},{"url":"/paper/imbalanced-continual-learning-with","slug":"imbalanced-continual-learning-with","title":"Imbalanced Continual Learning with Partitioning Reservoir Sampling","date":"2020-09-08","arxiv_id":"2009.03632","repositories_listed":1,"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/imbalanced-continual-learning-with#ran","syntology_url":"https://syntology.ai/paper/2009.03632","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.03632"}},"official":{"repos":["cdjkim/PRS"],"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/item-tagging-for-information-retrieval-a","slug":"item-tagging-for-information-retrieval-a","title":"Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach","date":"2020-08-26","arxiv_id":"2008.11567","repositories_listed":1,"syntology":null},{"url":"/paper/fixing-localization-errors-to-improve-image","slug":"fixing-localization-errors-to-improve-image","title":"Fixing Localization Errors to Improve Image Classification","date":"2020-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/oblique-predictive-clustering-trees","slug":"oblique-predictive-clustering-trees","title":"Oblique Predictive Clustering Trees","date":"2020-07-27","arxiv_id":"2007.13617","repositories_listed":1,"syntology":null},{"url":"/paper/distribution-balanced-loss-for-multi-label","slug":"distribution-balanced-loss-for-multi-label","title":"Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets","date":"2020-07-19","arxiv_id":"2007.09654","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-word-sense-disambiguation-in","slug":"multimodal-word-sense-disambiguation-in","title":"Multimodal Word Sense Disambiguation in Creative Practice","date":"2020-07-15","arxiv_id":"2007.07758","repositories_listed":1,"syntology":null},{"url":"/paper/emoji-prediction-extensions-and-benchmarking","slug":"emoji-prediction-extensions-and-benchmarking","title":"Emoji Prediction: Extensions and Benchmarking","date":"2020-07-14","arxiv_id":"2007.07389","repositories_listed":1,"syntology":null},{"url":"/paper/disentangled-variational-autoencoder-based","slug":"disentangled-variational-autoencoder-based","title":"Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model","date":"2020-07-12","arxiv_id":"2007.06126","repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-image-recognition-with-multi","slug":"multi-label-image-recognition-with-multi","title":"Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition","date":"2020-07-03","arxiv_id":"2007.01755","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-disaster-related-multi-label","slug":"cross-lingual-disaster-related-multi-label","title":"Cross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-gradient-boosted-multi-label","slug":"learning-gradient-boosted-multi-label","title":"Learning Gradient Boosted Multi-label Classification Rules","date":"2020-06-23","arxiv_id":"2006.13346","repositories_listed":1,"syntology":null},{"url":"/paper/an-integer-linear-programming-framework-for","slug":"an-integer-linear-programming-framework-for","title":"An Integer Linear Programming Framework for Mining Constraints from Data","date":"2020-06-18","arxiv_id":"2006.10836","repositories_listed":1,"syntology":null},{"url":"/paper/extreme-gradient-boosted-multi-label-trees","slug":"extreme-gradient-boosted-multi-label-trees","title":"Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains","date":"2020-06-15","arxiv_id":"2006.08094","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-label-semantics-for-predicting","slug":"modeling-label-semantics-for-predicting","title":"Modeling Label Semantics for Predicting Emotional Reactions","date":"2020-06-09","arxiv_id":"2006.05489","repositories_listed":1,"syntology":null},{"url":"/paper/infinitewalk-deep-network-embeddings-as","slug":"infinitewalk-deep-network-embeddings-as","title":"InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity","date":"2020-05-29","arxiv_id":"2006.00094","repositories_listed":1,"syntology":null},{"url":"/paper/a-robust-experimental-evaluation-of-automated","slug":"a-robust-experimental-evaluation-of-automated","title":"A Robust Experimental Evaluation of Automated Multi-Label Classification Methods","date":"2020-05-16","arxiv_id":"2005.08083","repositories_listed":1,"syntology":null},{"url":"/paper/dense-caption-matching-and-frame-selection","slug":"dense-caption-matching-and-frame-selection","title":"Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA","date":"2020-05-13","arxiv_id":"2005.06409","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":6,"n_ran_checked":8,"n_instrument":1,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"9 ran (of which 6 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/dense-caption-matching-and-frame-selection#ran","syntology_url":"https://syntology.ai/paper/2005.06409","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.06409"}},"official":{"repos":["hyounghk/VideoQADenseCapFrameGate-ACL2020"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":6,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/adversarial-learning-for-personalized-tag","slug":"adversarial-learning-for-personalized-tag","title":"Adversarial Learning for Personalized Tag Recommendation","date":"2020-04-01","arxiv_id":"2004.00698","repositories_listed":1,"syntology":null},{"url":"/paper/frimcla-a-framework-for-image-classification","slug":"frimcla-a-framework-for-image-classification","title":"FrImCla: A Framework for Image Classification Using Traditional and Transfer Learning Techniques","date":"2020-03-13","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-neural-diarization-reformulating","slug":"end-to-end-neural-diarization-reformulating","title":"End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification","date":"2020-02-24","arxiv_id":"2003.02966","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-classification-of-enzyme","slug":"hierarchical-classification-of-enzyme","title":"Enzyme promiscuity prediction using hierarchy-informed multi-label classification","date":"2020-02-18","arxiv_id":"2002.07327","repositories_listed":1,"syntology":null},{"url":"/paper/chexclusion-fairness-gaps-in-deep-chest-x-ray","slug":"chexclusion-fairness-gaps-in-deep-chest-x-ray","title":"CheXclusion: Fairness gaps in deep chest X-ray classifiers","date":"2020-02-14","arxiv_id":"2003.00827","repositories_listed":1,"syntology":null},{"url":"/paper/split-learning-for-collaborative-deep","slug":"split-learning-for-collaborative-deep","title":"Split Learning for collaborative deep learning in healthcare","date":"2019-12-27","arxiv_id":"1912.12115","repositories_listed":1,"syntology":null},{"url":"/paper/orderless-recurrent-models-for-multi-label","slug":"orderless-recurrent-models-for-multi-label","title":"Orderless Recurrent Models for Multi-label Classification","date":"2019-11-22","arxiv_id":"1911.09996","repositories_listed":1,"syntology":null},{"url":"/paper/macro-f1-and-macro-f1","slug":"macro-f1-and-macro-f1","title":"Macro F1 and Macro F1","date":"2019-11-08","arxiv_id":"1911.03347","repositories_listed":1,"syntology":null},{"url":"/paper/joint-ranking-svm-and-binary-relevance-with","slug":"joint-ranking-svm-and-binary-relevance-with","title":"Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification","date":"2019-11-05","arxiv_id":"1911.01658","repositories_listed":1,"syntology":null},{"url":"/paper/collaborative-graph-walk-for-semi-supervised","slug":"collaborative-graph-walk-for-semi-supervised","title":"Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification","date":"2019-10-22","arxiv_id":"1910.09706","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-learning-approach-to-approximate","slug":"supervised-learning-approach-to-approximate","title":"A Multilabel Classification Framework for Approximate Nearest Neighbor Search","date":"2019-10-18","arxiv_id":"1910.08322","repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-categorization-of-accounts-of","slug":"multi-label-categorization-of-accounts-of","title":"Multi-label Categorization of Accounts of Sexism using a Neural Framework","date":"2019-10-10","arxiv_id":"1910.04602","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-neural-speaker-diarization-with-1","slug":"end-to-end-neural-speaker-diarization-with-1","title":"End-to-End Neural Speaker Diarization with Permutation-Free Objectives","date":"2019-09-12","arxiv_id":"1909.05952","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-preserving-bandits","slug":"privacy-preserving-bandits","title":"Privacy-Preserving Bandits","date":"2019-09-10","arxiv_id":"1909.04421","repositories_listed":1,"syntology":null},{"url":"/paper/order-free-learning-alleviating-exposure-bias","slug":"order-free-learning-alleviating-exposure-bias","title":"Order-free Learning Alleviating Exposure Bias in Multi-label Classification","date":"2019-09-08","arxiv_id":"1909.03434","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-discovery-of-expressive-multi-label","slug":"efficient-discovery-of-expressive-multi-label","title":"Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning","date":"2019-08-19","arxiv_id":"1908.06874","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-trade-off-between-consistency-and","slug":"on-the-trade-off-between-consistency-and","title":"On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics","date":"2019-08-08","arxiv_id":"1908.03032","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-learning-by-disentangling-and","slug":"semi-supervised-learning-by-disentangling-and","title":"Semi-Supervised Learning by Disentangling and Self-Ensembling Over Stochastic Latent Space","date":"2019-07-22","arxiv_id":"1907.09607","repositories_listed":1,"syntology":null}],"record_sha256":"e4e60dff37c93ad10f0fddc45056301f6cdd8efd7630cb20774934d9e88d22ed","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}