{"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/4","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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":26,"rows_per_page":100,"rows":[301,400],"of":2574,"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","prev":"/task/binary-classification/papers/3","next":"/task/binary-classification/papers/5","papers":[{"url":"/paper/automated-chest-x-ray-report-generator-using","slug":"automated-chest-x-ray-report-generator-using","title":"Automated Chest X-Ray Report Generator Using Multi-Model Deep Learning Approach","date":"2023-09-28","arxiv_id":"2310.05969","repositories_listed":1,"syntology":null},{"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/boolformer-symbolic-regression-of-logic","slug":"boolformer-symbolic-regression-of-logic","title":"Boolformer: Symbolic Regression of Logic Functions with Transformers","date":"2023-09-21","arxiv_id":"2309.12207","repositories_listed":1,"syntology":null},{"url":"/paper/outlier-robust-adversarial-training","slug":"outlier-robust-adversarial-training","title":"Outlier Robust Adversarial Training","date":"2023-09-10","arxiv_id":"2309.05145","repositories_listed":1,"syntology":{"n":5,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"0 ran · 5 unverified","sample_list":"/paper/outlier-robust-adversarial-training#ran","syntology_url":"https://syntology.ai/paper/2309.05145","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.05145"}},"official":{"repos":["discovershu/orat"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":[]}}},{"url":"/paper/community-based-hierarchical-positive","slug":"community-based-hierarchical-positive","title":"Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for Chronic Disease Prediction","date":"2023-09-06","arxiv_id":"2309.03386","repositories_listed":1,"syntology":null},{"url":"/paper/distributed-variational-inference-for-online","slug":"distributed-variational-inference-for-online","title":"Distributed Variational Inference for Online Supervised Learning","date":"2023-09-05","arxiv_id":"2309.02606","repositories_listed":1,"syntology":null},{"url":"/paper/doppelgangers-learning-to-disambiguate-images","slug":"doppelgangers-learning-to-disambiguate-images","title":"Doppelgangers: Learning to Disambiguate Images of Similar Structures","date":"2023-09-05","arxiv_id":"2309.02420","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-computation-of-predictive","slug":"efficient-computation-of-predictive","title":"Efficient computation of predictive probabilities in probit models via expectation propagation","date":"2023-09-04","arxiv_id":"2309.01630","repositories_listed":1,"syntology":null},{"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/bridging-cross-task-protocol-inconsistency","slug":"bridging-cross-task-protocol-inconsistency","title":"Bridging Cross-task Protocol Inconsistency for Distillation in Dense Object Detection","date":"2023-08-28","arxiv_id":"2308.14286","repositories_listed":1,"syntology":null},{"url":"/paper/ummaformer-a-universal-multimodal-adaptive-1","slug":"ummaformer-a-universal-multimodal-adaptive-1","title":"UMMAFormer: A Universal Multimodal-adaptive Transformer Framework for Temporal Forgery Localization","date":"2023-08-28","arxiv_id":"2308.14395","repositories_listed":1,"syntology":null},{"url":"/paper/class-binarization-to-neuroevolution-for","slug":"class-binarization-to-neuroevolution-for","title":"Class Binarization to NeuroEvolution for Multiclass Classification","date":"2023-08-26","arxiv_id":"2308.13876","repositories_listed":1,"syntology":null},{"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/co-speech-gesture-detection-through-multi","slug":"co-speech-gesture-detection-through-multi","title":"Co-Speech Gesture Detection through Multi-Phase Sequence Labeling","date":"2023-08-21","arxiv_id":"2308.10680","repositories_listed":1,"syntology":null},{"url":"/paper/high-performance-computing-applied-to","slug":"high-performance-computing-applied-to","title":"High Performance Computing Applied to Logistic Regression: A CPU and GPU Implementation Comparison","date":"2023-08-19","arxiv_id":"2308.10037","repositories_listed":1,"syntology":null},{"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/chatgpt-healthprompt-harnessing-the-power-of","slug":"chatgpt-healthprompt-harnessing-the-power-of","title":"ChatGPT-HealthPrompt. Harnessing the Power of XAI in Prompt-Based Healthcare Decision Support using ChatGPT","date":"2023-08-17","arxiv_id":"2308.09731","repositories_listed":1,"syntology":null},{"url":"/paper/kernel-based-tests-for-likelihood-free","slug":"kernel-based-tests-for-likelihood-free","title":"Kernel-Based Tests for Likelihood-Free Hypothesis Testing","date":"2023-08-17","arxiv_id":"2308.09043","repositories_listed":1,"syntology":null},{"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/a-cross-domain-evaluation-of-approaches-for","slug":"a-cross-domain-evaluation-of-approaches-for","title":"A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction","date":"2023-08-07","arxiv_id":"2308.03891","repositories_listed":1,"syntology":null},{"url":"/paper/adapting-to-change-robust-counterfactual","slug":"adapting-to-change-robust-counterfactual","title":"Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes","date":"2023-08-04","arxiv_id":"2308.02353","repositories_listed":1,"syntology":null},{"url":"/paper/fpr-estimation-for-fraud-detection-in-the","slug":"fpr-estimation-for-fraud-detection-in-the","title":"FPR Estimation for Fraud Detection in the Presence of Class-Conditional Label Noise","date":"2023-08-04","arxiv_id":"2308.02695","repositories_listed":1,"syntology":null},{"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/model-calibration-in-dense-classification","slug":"model-calibration-in-dense-classification","title":"Model Calibration in Dense Classification with Adaptive Label Perturbation","date":"2023-07-25","arxiv_id":"2307.13539","repositories_listed":1,"syntology":null},{"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/the-imitation-game-detecting-human-and-ai","slug":"the-imitation-game-detecting-human-and-ai","title":"The Imitation Game: Detecting Human and AI-Generated Texts in the Era of ChatGPT and BARD","date":"2023-07-22","arxiv_id":"2307.12166","repositories_listed":1,"syntology":null},{"url":"/paper/finding-optimal-diverse-feature-sets-with","slug":"finding-optimal-diverse-feature-sets-with","title":"Finding Optimal Diverse Feature Sets with Alternative Feature Selection","date":"2023-07-21","arxiv_id":"2307.11607","repositories_listed":1,"syntology":null},{"url":"/paper/more-measurement-and-correlation-based","slug":"more-measurement-and-correlation-based","title":"MORE: Measurement and Correlation Based Variational Quantum Circuit for Multi-classification","date":"2023-07-21","arxiv_id":"2307.11875","repositories_listed":1,"syntology":null},{"url":"/paper/ord2seq-regard-ordinal-regression-as-label","slug":"ord2seq-regard-ordinal-regression-as-label","title":"Ord2Seq: Regarding Ordinal Regression as Label Sequence Prediction","date":"2023-07-18","arxiv_id":"2307.09004","repositories_listed":1,"syntology":null},{"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/exploring-binary-classification-loss-for","slug":"exploring-binary-classification-loss-for","title":"Exploring Binary Classification Loss For Speaker Verification","date":"2023-07-17","arxiv_id":"2307.08205","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-of-chatgpt-and-the-role-of","slug":"fairness-of-chatgpt-and-the-role-of","title":"Fairness of ChatGPT and the Role Of Explainable-Guided Prompts","date":"2023-07-14","arxiv_id":"2307.11761","repositories_listed":1,"syntology":null},{"url":"/paper/decorrelation-using-optimal-transport","slug":"decorrelation-using-optimal-transport","title":"Decorrelation using Optimal Transport","date":"2023-07-11","arxiv_id":"2307.05187","repositories_listed":1,"syntology":null},{"url":"/paper/a-framework-for-identifying-depression-on","slug":"a-framework-for-identifying-depression-on","title":"A Framework for Identifying Depression on Social Media: MentalRiskES@IberLEF 2023","date":"2023-06-28","arxiv_id":"2306.16125","repositories_listed":1,"syntology":null},{"url":"/paper/dermoscopic-dark-corner-artifacts-removal","slug":"dermoscopic-dark-corner-artifacts-removal","title":"Dermoscopic Dark Corner Artifacts Removal: Friend or Foe?","date":"2023-06-23","arxiv_id":"2306.13446","repositories_listed":1,"syntology":null},{"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/fairness-in-multi-task-learning-via","slug":"fairness-in-multi-task-learning-via","title":"Fairness in Multi-Task Learning via Wasserstein Barycenters","date":"2023-06-16","arxiv_id":"2306.10155","repositories_listed":1,"syntology":null},{"url":"/paper/on-orderings-of-probability-vectors-and","slug":"on-orderings-of-probability-vectors-and","title":"On Orderings of Probability Vectors and Unsupervised Performance Estimation","date":"2023-06-16","arxiv_id":"2306.10160","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-confusion-matrix-for","slug":"hierarchical-confusion-matrix-for","title":"Hierarchical confusion matrix for classification performance evaluation","date":"2023-06-15","arxiv_id":"2306.09461","repositories_listed":1,"syntology":null},{"url":"/paper/re-benchmarking-pool-based-active-learning","slug":"re-benchmarking-pool-based-active-learning","title":"Re-Benchmarking Pool-Based Active Learning for Binary Classification","date":"2023-06-15","arxiv_id":"2306.08954","repositories_listed":1,"syntology":null},{"url":"/paper/nocola-the-norwegian-corpus-of-linguistic","slug":"nocola-the-norwegian-corpus-of-linguistic","title":"NoCoLA: The Norwegian Corpus of Linguistic Acceptability","date":"2023-06-13","arxiv_id":"2306.07790","repositories_listed":1,"syntology":null},{"url":"/paper/strokes2surface-recovering-curve-networks","slug":"strokes2surface-recovering-curve-networks","title":"Strokes2Surface: Recovering Curve Networks From 4D Architectural Design Sketches","date":"2023-06-12","arxiv_id":"2306.07220","repositories_listed":1,"syntology":null},{"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/the-risks-of-recourse-in-binary","slug":"the-risks-of-recourse-in-binary","title":"The Risks of Recourse in Binary Classification","date":"2023-06-01","arxiv_id":"2306.00497","repositories_listed":1,"syntology":null},{"url":"/paper/adapterem-pre-trained-language-model","slug":"adapterem-pre-trained-language-model","title":"AdapterEM: Pre-trained Language Model Adaptation for Generalized Entity Matching using Adapter-tuning","date":"2023-05-30","arxiv_id":"2305.18725","repositories_listed":1,"syntology":null},{"url":"/paper/relabel-minimal-training-subset-to-flip-a","slug":"relabel-minimal-training-subset-to-flip-a","title":"Relabeling Minimal Training Subset to Flip a Prediction","date":"2023-05-22","arxiv_id":"2305.12809","repositories_listed":1,"syntology":null},{"url":"/paper/outage-performance-and-novel-loss-function","slug":"outage-performance-and-novel-loss-function","title":"Outage Performance and Novel Loss Function for an ML-Assisted Resource Allocation: An Exact Analytical Framework","date":"2023-05-16","arxiv_id":"2305.09739","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-noise-augmented-training-for","slug":"understanding-noise-augmented-training-for","title":"Understanding Noise-Augmented Training for Randomized Smoothing","date":"2023-05-08","arxiv_id":"2305.04746","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/understanding-noise-augmented-training-for#ran","syntology_url":"https://syntology.ai/paper/2305.04746","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04746"}},"official":{"repos":["ambarpal/randomized-smoothing"],"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/learning-decision-trees-with-gradient-descent","slug":"learning-decision-trees-with-gradient-descent","title":"GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent","date":"2023-05-05","arxiv_id":"2305.03515","repositories_listed":1,"syntology":null},{"url":"/paper/glitch-in-the-matrix-a-large-scale-benchmark","slug":"glitch-in-the-matrix-a-large-scale-benchmark","title":"Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization","date":"2023-05-03","arxiv_id":"2305.01979","repositories_listed":1,"syntology":null},{"url":"/paper/ucf-uncovering-common-features-for","slug":"ucf-uncovering-common-features-for","title":"UCF: Uncovering Common Features for Generalizable Deepfake Detection","date":"2023-04-27","arxiv_id":"2304.13949","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-transformer-encoders-for-vision","slug":"recurrent-transformer-encoders-for-vision","title":"Vision-based Estimation of Fatigue and Engagement in Cognitive Training Sessions","date":"2023-04-24","arxiv_id":"2304.12470","repositories_listed":1,"syntology":null},{"url":"/paper/stnet-spatial-and-temporal-feature-fusion","slug":"stnet-spatial-and-temporal-feature-fusion","title":"STNet: Spatial and Temporal feature fusion network for change detection in remote sensing images","date":"2023-04-22","arxiv_id":"2304.11422","repositories_listed":1,"syntology":null},{"url":"/paper/rolling-lookahead-learning-for-optimal","slug":"rolling-lookahead-learning-for-optimal","title":"Rolling Lookahead Learning for Optimal Classification Trees","date":"2023-04-21","arxiv_id":"2304.10830","repositories_listed":1,"syntology":null},{"url":"/paper/misrobaerta-transformers-versus","slug":"misrobaerta-transformers-versus","title":"MisRoBÆRTa: Transformers versus Misinformation","date":"2023-04-16","arxiv_id":"2304.07759","repositories_listed":1,"syntology":null},{"url":"/paper/a-contrastive-method-based-on-elevation-data","slug":"a-contrastive-method-based-on-elevation-data","title":"Enhancing Self-Supervised Learning for Remote Sensing with Elevation Data: A Case Study with Scarce And High Level Semantic Labels","date":"2023-04-13","arxiv_id":"2304.06857","repositories_listed":1,"syntology":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/weakly-supervised-intracranial-hemorrhage","slug":"weakly-supervised-intracranial-hemorrhage","title":"Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning","date":"2023-04-11","arxiv_id":"2304.04902","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-multi-labeled-bengali-toxic","slug":"interpretable-multi-labeled-bengali-toxic","title":"Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning","date":"2023-04-08","arxiv_id":"2304.04087","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-and-grounding-multi-modal-media","slug":"detecting-and-grounding-multi-modal-media","title":"Detecting and Grounding Multi-Modal Media Manipulation","date":"2023-04-05","arxiv_id":"2304.02556","repositories_listed":1,"syntology":null},{"url":"/paper/a-video-based-end-to-end-pipeline-for-non","slug":"a-video-based-end-to-end-pipeline-for-non","title":"A Video-based End-to-end Pipeline for Non-nutritive Sucking Action Recognition and Segmentation in Young Infants","date":"2023-03-29","arxiv_id":"2303.16867","repositories_listed":1,"syntology":null},{"url":"/paper/biologically-primed-deep-neural-network","slug":"biologically-primed-deep-neural-network","title":"Exploring the Interplay Between Colorectal Cancer Subtypes Genomic Variants and Cellular Morphology: A Deep-Learning Approach","date":"2023-03-26","arxiv_id":"2303.14703","repositories_listed":1,"syntology":null},{"url":"/paper/transfer-learning-method-in-the-problem-of","slug":"transfer-learning-method-in-the-problem-of","title":"Transfer learning method in the problem of binary classification of chest X-rays","date":"2023-03-19","arxiv_id":"2303.10601","repositories_listed":1,"syntology":null},{"url":"/paper/pulsnar-positive-unlabeled-learning-selected","slug":"pulsnar-positive-unlabeled-learning-selected","title":"Positive Unlabeled Learning Selected Not At Random (PULSNAR): class proportion estimation when the SCAR assumption does not hold","date":"2023-03-14","arxiv_id":"2303.08269","repositories_listed":1,"syntology":null},{"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/interactive-segmentation-as-gaussian-process","slug":"interactive-segmentation-as-gaussian-process","title":"Interactive Segmentation as Gaussian Process Classification","date":"2023-02-28","arxiv_id":"2302.14578","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/interactive-segmentation-as-gaussian-process#ran","syntology_url":"https://syntology.ai/paper/2302.14578","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.14578"}},"official":{"repos":["zmhhmz/gpcis_cvpr2023"],"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/mvmtnet-a-multi-variate-multi-modal","slug":"mvmtnet-a-multi-variate-multi-modal","title":"MVMTnet: A Multi-variate Multi-modal Transformer for Multi-class Classification of Cardiac Irregularities Using ECG Waveforms and Clinical Notes","date":"2023-02-21","arxiv_id":"2302.11021","repositories_listed":1,"syntology":null},{"url":"/paper/lero-a-learning-to-rank-query-optimizer","slug":"lero-a-learning-to-rank-query-optimizer","title":"Lero: A Learning-to-Rank Query Optimizer","date":"2023-02-14","arxiv_id":"2302.06873","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-of-data-augmentation-and-loss","slug":"evaluation-of-data-augmentation-and-loss","title":"Evaluation of Data Augmentation and Loss Functions in Semantic Image Segmentation for Drilling Tool Wear Detection","date":"2023-02-10","arxiv_id":"2302.05262","repositories_listed":1,"syntology":null},{"url":"/paper/the-role-of-codeword-to-class-assignments-in","slug":"the-role-of-codeword-to-class-assignments-in","title":"The Role of Codeword-to-Class Assignments in Error-Correcting Codes: An Empirical Study","date":"2023-02-10","arxiv_id":"2302.05334","repositories_listed":1,"syntology":null},{"url":"/paper/hybrid-genetic-optimisation-for-quantum","slug":"hybrid-genetic-optimisation-for-quantum","title":"Hybrid Genetic Optimisation for Quantum Feature Map Design","date":"2023-02-06","arxiv_id":"2302.02980","repositories_listed":1,"syntology":null},{"url":"/paper/longformer-longitudinal-transformer-for","slug":"longformer-longitudinal-transformer-for","title":"Longformer: Longitudinal Transformer for Alzheimer's Disease Classification with Structural MRIs","date":"2023-02-02","arxiv_id":"2302.00901","repositories_listed":1,"syntology":null},{"url":"/paper/neural-gas-network-image-features-and","slug":"neural-gas-network-image-features-and","title":"Neural Gas Network Image Features and Segmentation for Brain Tumor Detection Using Magnetic Resonance Imaging Data","date":"2023-01-28","arxiv_id":"2301.12176","repositories_listed":1,"syntology":null},{"url":"/paper/variance-self-consistency-and-arbitrariness","slug":"variance-self-consistency-and-arbitrariness","title":"Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification","date":"2023-01-27","arxiv_id":"2301.11562","repositories_listed":1,"syntology":null},{"url":"/paper/long-tail-detection-with-effective-class","slug":"long-tail-detection-with-effective-class","title":"Long-tail Detection with Effective Class-Margins","date":"2023-01-23","arxiv_id":"2301.09724","repositories_listed":1,"syntology":null},{"url":"/paper/critical-perspectives-a-benchmark-revealing","slug":"critical-perspectives-a-benchmark-revealing","title":"Critical Perspectives: A Benchmark Revealing Pitfalls in PerspectiveAPI","date":"2023-01-05","arxiv_id":"2301.01874","repositories_listed":1,"syntology":null},{"url":"/paper/interactive-segmentation-as-gaussion-process","slug":"interactive-segmentation-as-gaussion-process","title":"Interactive Segmentation As Gaussion Process Classification","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ecg-based-electrolyte-prediction-evaluating","slug":"ecg-based-electrolyte-prediction-evaluating","title":"ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods","date":"2022-12-21","arxiv_id":"2212.13890","repositories_listed":1,"syntology":null},{"url":"/paper/de-risking-carbon-capture-and-sequestration","slug":"de-risking-carbon-capture-and-sequestration","title":"De-risking Carbon Capture and Sequestration with Explainable CO2 Leakage Detection in Time-lapse Seismic Monitoring Images","date":"2022-12-16","arxiv_id":"2212.08596","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-ml-for-imbalanced-data","slug":"interpretable-ml-for-imbalanced-data","title":"Interpretable ML for Imbalanced Data","date":"2022-12-15","arxiv_id":"2212.07743","repositories_listed":1,"syntology":null},{"url":"/paper/unfolding-local-growth-rate-estimates-for","slug":"unfolding-local-growth-rate-estimates-for","title":"Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection","date":"2022-12-13","arxiv_id":"2212.06776","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-structure-for-improved","slug":"leveraging-structure-for-improved","title":"Leveraging Structure for Improved Classification of Grouped Biased Data","date":"2022-12-07","arxiv_id":"2212.03697","repositories_listed":1,"syntology":null},{"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/split-pu-hardness-aware-training-strategy-for","slug":"split-pu-hardness-aware-training-strategy-for","title":"Split-PU: Hardness-aware Training Strategy for Positive-Unlabeled Learning","date":"2022-11-30","arxiv_id":"2211.16756","repositories_listed":1,"syntology":null},{"url":"/paper/positive-unlabeled-learning-with-tensor","slug":"positive-unlabeled-learning-with-tensor","title":"Positive unlabeled learning with tensor networks","date":"2022-11-25","arxiv_id":"2211.14085","repositories_listed":1,"syntology":null},{"url":"/paper/lifting-weak-supervision-to-structured","slug":"lifting-weak-supervision-to-structured","title":"Lifting Weak Supervision To Structured Prediction","date":"2022-11-24","arxiv_id":"2211.13375","repositories_listed":1,"syntology":null},{"url":"/paper/finding-active-galactic-nuclei-through-fink","slug":"finding-active-galactic-nuclei-through-fink","title":"Finding active galactic nuclei through Fink","date":"2022-11-20","arxiv_id":"2211.10987","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/finding-active-galactic-nuclei-through-fink#ran","syntology_url":"https://syntology.ai/paper/2211.10987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.10987"}},"official":{"repos":["astrolabsoftware/fink-science"],"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/patch-level-gaze-distribution-prediction-for","slug":"patch-level-gaze-distribution-prediction-for","title":"Patch-level Gaze Distribution Prediction for Gaze Following","date":"2022-11-20","arxiv_id":"2211.11062","repositories_listed":1,"syntology":null},{"url":"/paper/okapi-generalising-better-by-making","slug":"okapi-generalising-better-by-making","title":"Okapi: Generalising Better by Making Statistical Matches Match","date":"2022-11-07","arxiv_id":"2211.05236","repositories_listed":1,"syntology":null},{"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/a-study-of-uncertainty-quantification-in","slug":"a-study-of-uncertainty-quantification-in","title":"On double-descent in uncertainty quantification in overparametrized models","date":"2022-10-23","arxiv_id":"2210.12760","repositories_listed":1,"syntology":null},{"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/online-lidar-camera-extrinsic-parameters-self","slug":"online-lidar-camera-extrinsic-parameters-self","title":"Online LiDAR-Camera Extrinsic Parameters Self-checking","date":"2022-10-19","arxiv_id":"2210.10537","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-differentially-private-and-fair","slug":"stochastic-differentially-private-and-fair","title":"Stochastic Differentially Private and Fair Learning","date":"2022-10-17","arxiv_id":"2210.08781","repositories_listed":1,"syntology":null},{"url":"/paper/systematic-evaluation-of-predictive-fairness","slug":"systematic-evaluation-of-predictive-fairness","title":"Systematic Evaluation of Predictive Fairness","date":"2022-10-17","arxiv_id":"2210.08758","repositories_listed":1,"syntology":null},{"url":"/paper/john-is-50-years-old-can-his-son-be-65","slug":"john-is-50-years-old-can-his-son-be-65","title":"\"John is 50 years old, can his son be 65?\" Evaluating NLP Models' Understanding of Feasibility","date":"2022-10-14","arxiv_id":"2210.07471","repositories_listed":1,"syntology":null},{"url":"/paper/prediction-of-drug-effectiveness-in","slug":"prediction-of-drug-effectiveness-in","title":"Prediction of drug effectiveness in rheumatoid arthritis patients based on machine learning algorithms","date":"2022-10-14","arxiv_id":"2210.08016","repositories_listed":1,"syntology":null},{"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"]}}}],"record_sha256":"d5fbdcce74cc4f1b692e15a2b1ed7a60327c4f2181ab3a73b9baba9e67d2b495","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}