{"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/2","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":2,"pages_in_order":26,"rows_per_page":100,"rows":[101,200],"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","next":"/task/binary-classification/papers/3","papers":[{"url":"/paper/lgar-zero-shot-llm-guided-neural-ranking-for","slug":"lgar-zero-shot-llm-guided-neural-ranking-for","title":"LGAR: Zero-Shot LLM-Guided Neural Ranking for Abstract Screening in Systematic Literature Reviews","date":"2025-05-30","arxiv_id":"2505.24757","repositories_listed":1,"syntology":null},{"url":"/paper/patchdemux-a-certifiably-robust-framework-for","slug":"patchdemux-a-certifiably-robust-framework-for","title":"PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches","date":"2025-05-30","arxiv_id":"2505.24703","repositories_listed":1,"syntology":null},{"url":"/paper/rhapsody-a-dataset-for-highlight-detection-in","slug":"rhapsody-a-dataset-for-highlight-detection-in","title":"Rhapsody: A Dataset for Highlight Detection in Podcasts","date":"2025-05-26","arxiv_id":"2505.19429","repositories_listed":1,"syntology":null},{"url":"/paper/token-level-accept-or-reject-a-micro","slug":"token-level-accept-or-reject-a-micro","title":"Token-level Accept or Reject: A Micro Alignment Approach for Large Language Models","date":"2025-05-26","arxiv_id":"2505.19743","repositories_listed":1,"syntology":null},{"url":"/paper/xdementnet-an-explainable-attention-based","slug":"xdementnet-an-explainable-attention-based","title":"XDementNET: An Explainable Attention Based Deep Convolutional Network to Detect Alzheimer Progression from MRI data","date":"2025-05-20","arxiv_id":"2505.13906","repositories_listed":1,"syntology":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/softpq-robust-instance-segmentation","slug":"softpq-robust-instance-segmentation","title":"SoftPQ: Robust Instance Segmentation Evaluation via Soft Matching and Tunable Thresholds","date":"2025-05-17","arxiv_id":"2505.12155","repositories_listed":1,"syntology":null},{"url":"/paper/venusx-unlocking-fine-grained-functional","slug":"venusx-unlocking-fine-grained-functional","title":"VenusX: Unlocking Fine-Grained Functional Understanding of Proteins","date":"2025-05-17","arxiv_id":"2505.11812","repositories_listed":1,"syntology":null},{"url":"/paper/comparing-llm-text-annotation-skills-a-study","slug":"comparing-llm-text-annotation-skills-a-study","title":"Comparing LLM Text Annotation Skills: A Study on Human Rights Violations in Social Media Data","date":"2025-05-15","arxiv_id":"2505.10260","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-multi-modal-large-language-model-v","slug":"zero-shot-multi-modal-large-language-model-v","title":"Zero-Shot Multi-modal Large Language Model v.s. Supervised Deep Learning: A Comparative Study on CT-Based Intracranial Hemorrhage Subtyping","date":"2025-05-14","arxiv_id":"2505.09252","repositories_listed":1,"syntology":null},{"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/cyber-security-data-science-machine-learning","slug":"cyber-security-data-science-machine-learning","title":"Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets","date":"2025-05-07","arxiv_id":"2505.04204","repositories_listed":1,"syntology":null},{"url":"/paper/anomalymatch-discovering-rare-objects-of","slug":"anomalymatch-discovering-rare-objects-of","title":"AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning","date":"2025-05-06","arxiv_id":"2505.03509","repositories_listed":1,"syntology":null},{"url":"/paper/decision-centric-fairness-evaluation-and","slug":"decision-centric-fairness-evaluation-and","title":"Decision-centric fairness: Evaluation and optimization for resource allocation problems","date":"2025-04-29","arxiv_id":"2504.20642","repositories_listed":1,"syntology":null},{"url":"/paper/bug-destiny-prediction-in-large-open-source","slug":"bug-destiny-prediction-in-large-open-source","title":"Bug Destiny Prediction in Large Open-Source Software Repositories through Sentiment Analysis and BERT Topic Modeling","date":"2025-04-22","arxiv_id":"2504.15972","repositories_listed":1,"syntology":null},{"url":"/paper/td-suite-all-batteries-included-framework-for","slug":"td-suite-all-batteries-included-framework-for","title":"TD-Suite: All Batteries Included Framework for Technical Debt Classification","date":"2025-04-15","arxiv_id":"2504.11085","repositories_listed":1,"syntology":null},{"url":"/paper/cal-or-no-cal-real-time-miscalibration","slug":"cal-or-no-cal-real-time-miscalibration","title":"Cal or No Cal? -- Real-Time Miscalibration Detection of LiDAR and Camera Sensors","date":"2025-03-31","arxiv_id":"2504.01040","repositories_listed":1,"syntology":null},{"url":"/paper/a-causal-framework-to-measure-and-mitigate","slug":"a-causal-framework-to-measure-and-mitigate","title":"A Causal Framework to Measure and Mitigate Non-binary Treatment Discrimination","date":"2025-03-28","arxiv_id":"2503.22454","repositories_listed":1,"syntology":null},{"url":"/paper/historical-ink-exploring-large-language","slug":"historical-ink-exploring-large-language","title":"Historical Ink: Exploring Large Language Models for Irony Detection in 19th-Century Spanish","date":"2025-03-28","arxiv_id":"2503.22585","repositories_listed":1,"syntology":null},{"url":"/paper/skdu-at-de-factify-4-0-natural-language","slug":"skdu-at-de-factify-4-0-natural-language","title":"SKDU at De-Factify 4.0: Natural Language Features for AI-Generated Text-Detection","date":"2025-03-28","arxiv_id":"2503.22338","repositories_listed":1,"syntology":null},{"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/scaled-supervision-is-an-implicit-lipschitz","slug":"scaled-supervision-is-an-implicit-lipschitz","title":"Scaled Supervision is an Implicit Lipschitz Regularizer","date":"2025-03-19","arxiv_id":"2503.14813","repositories_listed":1,"syntology":null},{"url":"/paper/bilingual-dual-head-deep-model-for-parkinson","slug":"bilingual-dual-head-deep-model-for-parkinson","title":"Bilingual Dual-Head Deep Model for Parkinson's Disease Detection from Speech","date":"2025-03-13","arxiv_id":"2503.10301","repositories_listed":1,"syntology":null},{"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/leveraging-large-language-models-to-address","slug":"leveraging-large-language-models-to-address","title":"Leveraging Large Language Models to Address Data Scarcity in Machine Learning: Applications in Graphene Synthesis","date":"2025-03-06","arxiv_id":"2503.04870","repositories_listed":1,"syntology":null},{"url":"/paper/biased-heritage-how-datasets-shape-models-in","slug":"biased-heritage-how-datasets-shape-models-in","title":"Biased Heritage: How Datasets Shape Models in Facial Expression Recognition","date":"2025-03-05","arxiv_id":"2503.03446","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-of-hate-speech-detection-using","slug":"evaluation-of-hate-speech-detection-using","title":"Evaluation of Hate Speech Detection Using Large Language Models and Geographical Contextualization","date":"2025-02-26","arxiv_id":"2502.19612","repositories_listed":1,"syntology":null},{"url":"/paper/keeping-up-with-dynamic-attackers-certifying","slug":"keeping-up-with-dynamic-attackers-certifying","title":"Keeping up with dynamic attackers: Certifying robustness to adaptive online data poisoning","date":"2025-02-23","arxiv_id":"2502.16737","repositories_listed":1,"syntology":null},{"url":"/paper/unveiling-the-capabilities-of-large-language","slug":"unveiling-the-capabilities-of-large-language","title":"Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement","date":"2025-02-10","arxiv_id":"2502.06207","repositories_listed":1,"syntology":null},{"url":"/paper/scbit-integrating-single-cell-transcriptomic","slug":"scbit-integrating-single-cell-transcriptomic","title":"scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based Prediction for Alzheimer's Disease Diagnosis","date":"2025-02-04","arxiv_id":"2502.02630","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-harassment-and-defamation-in","slug":"detecting-harassment-and-defamation-in","title":"Detecting harassment and defamation in cyberbullying with emotion-adaptive training","date":"2025-01-28","arxiv_id":"2501.16925","repositories_listed":1,"syntology":null},{"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/leveraging-graph-neural-networks-and-mobility","slug":"leveraging-graph-neural-networks-and-mobility","title":"Leveraging graph neural networks and mobility data for COVID-19 forecasting","date":"2025-01-20","arxiv_id":"2501.11711","repositories_listed":1,"syntology":null},{"url":"/paper/neural-codec-source-tracing-toward","slug":"neural-codec-source-tracing-toward","title":"Neural Codec Source Tracing: Toward Comprehensive Attribution in Open-Set Condition","date":"2025-01-11","arxiv_id":"2501.06514","repositories_listed":1,"syntology":null},{"url":"/paper/medical-artificial-intelligence-toolbox-mait","slug":"medical-artificial-intelligence-toolbox-mait","title":"Medical artificial intelligence toolbox (MAIT): an explainable machine learning framework for binary classification, survival modelling, and regression analyses","date":"2025-01-08","arxiv_id":"2501.04547","repositories_listed":1,"syntology":null},{"url":"/paper/a-decision-based-heterogenous-graph-attention","slug":"a-decision-based-heterogenous-graph-attention","title":"A Decision-Based Heterogenous Graph Attention Network for Multi-Class Fake News Detection","date":"2025-01-06","arxiv_id":"2501.03290","repositories_listed":1,"syntology":null},{"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/architecture-for-trajectory-based-fishing","slug":"architecture-for-trajectory-based-fishing","title":"Architecture for Trajectory-Based Fishing Ship Classification with AIS Data","date":"2025-01-03","arxiv_id":"2501.02038","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-filter-outlier-edges-in-global","slug":"learning-to-filter-outlier-edges-in-global","title":"Learning to Filter Outlier Edges in Global SfM","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-new-dataset-and-methodology-for-malicious","slug":"a-new-dataset-and-methodology-for-malicious","title":"A New Dataset and Methodology for Malicious URL Classification","date":"2024-12-31","arxiv_id":"2501.00356","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-ai-for-automatic-classification-of","slug":"leveraging-ai-for-automatic-classification-of","title":"Leveraging AI for Automatic Classification of PCOS Using Ultrasound Imaging","date":"2024-12-30","arxiv_id":"2501.01984","repositories_listed":1,"syntology":null},{"url":"/paper/progressive-boundary-guided-anomaly-synthesis","slug":"progressive-boundary-guided-anomaly-synthesis","title":"Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection","date":"2024-12-23","arxiv_id":"2412.17458","repositories_listed":1,"syntology":null},{"url":"/paper/interactive-classification-metrics-a","slug":"interactive-classification-metrics-a","title":"Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation","date":"2024-12-22","arxiv_id":"2412.17066","repositories_listed":1,"syntology":null},{"url":"/paper/froc-building-fair-roc-from-a-trained","slug":"froc-building-fair-roc-from-a-trained","title":"FROC: Building Fair ROC from a Trained Classifier","date":"2024-12-19","arxiv_id":"2412.14724","repositories_listed":1,"syntology":null},{"url":"/paper/electrocardiogram-based-diagnosis-of-liver","slug":"electrocardiogram-based-diagnosis-of-liver","title":"Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approach","date":"2024-12-04","arxiv_id":"2412.03717","repositories_listed":1,"syntology":null},{"url":"/paper/active-learning-via-classifier-impact-and","slug":"active-learning-via-classifier-impact-and","title":"Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval","date":"2024-12-03","arxiv_id":"2412.02310","repositories_listed":1,"syntology":null},{"url":"/paper/icpr-2024-competition-on-multilingual-claim","slug":"icpr-2024-competition-on-multilingual-claim","title":"ICPR 2024 Competition on Multilingual Claim-Span Identification","date":"2024-11-29","arxiv_id":"2411.19579","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-logistic-ensembles-with-recursive","slug":"dynamic-logistic-ensembles-with-recursive","title":"Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification","date":"2024-11-27","arxiv_id":"2411.18649","repositories_listed":1,"syntology":null},{"url":"/paper/breast-tumor-classification-using","slug":"breast-tumor-classification-using","title":"Breast Tumor Classification Using EfficientNet Deep Learning Model","date":"2024-11-26","arxiv_id":"2411.17870","repositories_listed":1,"syntology":null},{"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/advacheck-at-genai-detection-task-1-ai","slug":"advacheck-at-genai-detection-task-1-ai","title":"Advacheck at GenAI Detection Task 1: AI Detection Powered by Domain-Aware Multi-Tasking","date":"2024-11-18","arxiv_id":"2411.11736","repositories_listed":1,"syntology":null},{"url":"/paper/finding-strong-lottery-ticket-networks-with","slug":"finding-strong-lottery-ticket-networks-with","title":"Finding Strong Lottery Ticket Networks with Genetic Algorithms","date":"2024-11-07","arxiv_id":"2411.04658","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-large-language-models-for-1","slug":"investigating-large-language-models-for-1","title":"Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups","date":"2024-11-03","arxiv_id":"2411.01706","repositories_listed":1,"syntology":null},{"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/robin-a-transformer-based-model-for-risk-of","slug":"robin-a-transformer-based-model-for-risk-of","title":"RoBIn: A Transformer-Based Model For Risk Of Bias Inference With Machine Reading Comprehension","date":"2024-10-28","arxiv_id":"2410.21495","repositories_listed":1,"syntology":null},{"url":"/paper/detection-of-human-and-machine-authored-fake","slug":"detection-of-human-and-machine-authored-fake","title":"Detection of Human and Machine-Authored Fake News in Urdu","date":"2024-10-25","arxiv_id":"2410.19517","repositories_listed":1,"syntology":null},{"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/real-time-fake-news-from-adversarial-feedback","slug":"real-time-fake-news-from-adversarial-feedback","title":"Real-time Fake News from Adversarial Feedback","date":"2024-10-18","arxiv_id":"2410.14651","repositories_listed":1,"syntology":null},{"url":"/paper/unveiling-large-language-models-generated","slug":"unveiling-large-language-models-generated","title":"Unveiling Large Language Models Generated Texts: A Multi-Level Fine-Grained Detection Framework","date":"2024-10-18","arxiv_id":"2410.14231","repositories_listed":1,"syntology":null},{"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/weak-to-strong-generalization-beyond-accuracy","slug":"weak-to-strong-generalization-beyond-accuracy","title":"Weak-to-Strong Generalization beyond Accuracy: a Pilot Study in Safety, Toxicity, and Legal Reasoning","date":"2024-10-16","arxiv_id":"2410.12621","repositories_listed":1,"syntology":null},{"url":"/paper/geometric-inductive-biases-of-deep-networks","slug":"geometric-inductive-biases-of-deep-networks","title":"Geometric Inductive Biases of Deep Networks: The Role of Data and Architecture","date":"2024-10-15","arxiv_id":"2410.12025","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-line-search-for-optimizing-area","slug":"efficient-line-search-for-optimizing-area","title":"Efficient line search for optimizing Area Under the ROC Curve in gradient descent","date":"2024-10-11","arxiv_id":"2410.08635","repositories_listed":1,"syntology":null},{"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/ltntorch-pytorch-implementation-of-logic","slug":"ltntorch-pytorch-implementation-of-logic","title":"LTNtorch: PyTorch Implementation of Logic Tensor Networks","date":"2024-09-24","arxiv_id":"2409.16045","repositories_listed":1,"syntology":null},{"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/optimal-classification-based-anomaly","slug":"optimal-classification-based-anomaly","title":"Optimal Classification-based Anomaly Detection with Neural Networks: Theory and Practice","date":"2024-09-13","arxiv_id":"2409.08521","repositories_listed":1,"syntology":null},{"url":"/paper/sequential-classification-of-misinformation","slug":"sequential-classification-of-misinformation","title":"Sequential Classification of Misinformation","date":"2024-09-07","arxiv_id":"2409.04860","repositories_listed":1,"syntology":null},{"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/semantics-oriented-multitask-learning-for","slug":"semantics-oriented-multitask-learning-for","title":"Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach","date":"2024-08-29","arxiv_id":"2408.16305","repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-feature-space-augmentation-method-to","slug":"a-novel-feature-space-augmentation-method-to","title":"A Novel Feature Space Augmentation Method to Improve Classification Performance and Evaluation Reliability","date":"2024-08-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/deep-tree-based-retrieval-for-efficient","slug":"deep-tree-based-retrieval-for-efficient","title":"Learning Deep Tree-based Retriever for Efficient Recommendation: Theory and Method","date":"2024-08-21","arxiv_id":"2408.11345","repositories_listed":1,"syntology":null},{"url":"/paper/xdt-cxr-investigating-cross-disease","slug":"xdt-cxr-investigating-cross-disease","title":"XDT-CXR: Investigating Cross-Disease Transferability in Zero-Shot Binary Classification of Chest X-Rays","date":"2024-08-21","arxiv_id":"2408.11493","repositories_listed":1,"syntology":null},{"url":"/paper/p-svm-soft-margin-svms-with-p-norm-hinge-loss","slug":"p-svm-soft-margin-svms-with-p-norm-hinge-loss","title":"$p$SVM: Soft-margin SVMs with $p$-norm Hinge Loss","date":"2024-08-19","arxiv_id":"2408.09908","repositories_listed":1,"syntology":null},{"url":"/paper/protein-language-models-and-machine-learning","slug":"protein-language-models-and-machine-learning","title":"Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides","date":"2024-08-14","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/centralized-and-federated-heart-disease","slug":"centralized-and-federated-heart-disease","title":"Centralized and Federated Heart Disease Classification Models Using UCI Dataset and their Shapley-value Based Interpretability","date":"2024-08-12","arxiv_id":"2408.06183","repositories_listed":1,"syntology":null},{"url":"/paper/cautious-calibration-in-binary-classification","slug":"cautious-calibration-in-binary-classification","title":"Cautious Calibration in Binary Classification","date":"2024-08-09","arxiv_id":"2408.05120","repositories_listed":1,"syntology":null},{"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/probabilistic-scores-of-classifiers","slug":"probabilistic-scores-of-classifiers","title":"Probabilistic Scores of Classifiers, Calibration is not Enough","date":"2024-08-06","arxiv_id":"2408.03421","repositories_listed":1,"syntology":null},{"url":"/paper/2408-01163","slug":"2408-01163","title":"Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagery","date":"2024-08-02","arxiv_id":"2408.01163","repositories_listed":1,"syntology":null},{"url":"/paper/a-scalable-quantum-non-local-neural-network","slug":"a-scalable-quantum-non-local-neural-network","title":"A Scalable Quantum Non-local Neural Network for Image Classification","date":"2024-07-26","arxiv_id":"2407.18906","repositories_listed":1,"syntology":null},{"url":"/paper/distance-based-mutual-congestion-feature","slug":"distance-based-mutual-congestion-feature","title":"Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets","date":"2024-07-22","arxiv_id":"2407.15611","repositories_listed":1,"syntology":null},{"url":"/paper/patch-based-intuitive-multimodal-prototypes","slug":"patch-based-intuitive-multimodal-prototypes","title":"Patch-based Intuitive Multimodal Prototypes Network (PIMPNet) for Alzheimer's Disease classification","date":"2024-07-19","arxiv_id":"2407.14277","repositories_listed":1,"syntology":null},{"url":"/paper/turkish-delights-a-dataset-on-turkish","slug":"turkish-delights-a-dataset-on-turkish","title":"Turkish Delights: a Dataset on Turkish Euphemisms","date":"2024-07-17","arxiv_id":"2407.13040","repositories_listed":1,"syntology":null},{"url":"/paper/nullpointer-at-checkthat-2024-identifying","slug":"nullpointer-at-checkthat-2024-identifying","title":"Nullpointer at CheckThat! 2024: Identifying Subjectivity from Multilingual Text Sequence","date":"2024-07-14","arxiv_id":"2407.10252","repositories_listed":1,"syntology":null},{"url":"/paper/rigorous-probabilistic-guarantees-for-robust","slug":"rigorous-probabilistic-guarantees-for-robust","title":"Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations","date":"2024-07-10","arxiv_id":"2407.07482","repositories_listed":1,"syntology":null},{"url":"/paper/hard-attention-gates-with-gradient-routing","slug":"hard-attention-gates-with-gradient-routing","title":"Hard-Attention Gates with Gradient Routing for Endoscopic Image Computing","date":"2024-07-05","arxiv_id":"2407.04400","repositories_listed":1,"syntology":null},{"url":"/paper/mddbranchnet-a-deep-learning-model-for","slug":"mddbranchnet-a-deep-learning-model-for","title":"MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal","date":"2024-07-04","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/axial-attention-based-explainability-for","slug":"axial-attention-based-explainability-for","title":"AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans","date":"2024-07-02","arxiv_id":"2407.02418","repositories_listed":1,"syntology":null},{"url":"/paper/oxonfair-a-flexible-toolkit-for-algorithmic","slug":"oxonfair-a-flexible-toolkit-for-algorithmic","title":"OxonFair: A Flexible Toolkit for Algorithmic Fairness","date":"2024-06-30","arxiv_id":"2407.13710","repositories_listed":1,"syntology":null},{"url":"/paper/ehrmonize-a-framework-for-medical-concept","slug":"ehrmonize-a-framework-for-medical-concept","title":"EHRmonize: A Framework for Medical Concept Abstraction from Electronic Health Records using Large Language Models","date":"2024-06-28","arxiv_id":"2407.00242","repositories_listed":1,"syntology":null},{"url":"/paper/pairwise-difference-learning-for","slug":"pairwise-difference-learning-for","title":"Pairwise Difference Learning for Classification","date":"2024-06-28","arxiv_id":"2406.20031","repositories_listed":1,"syntology":null},{"url":"/paper/pathological-regularization-regimes-in","slug":"pathological-regularization-regimes-in","title":"Pathological Regularization Regimes in Classification Tasks","date":"2024-06-20","arxiv_id":"2406.14731","repositories_listed":1,"syntology":null},{"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/toxic-memes-a-survey-of-computational","slug":"toxic-memes-a-survey-of-computational","title":"Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicities","date":"2024-06-11","arxiv_id":"2406.07353","repositories_listed":1,"syntology":null},{"url":"/paper/logicode-an-llm-driven-framework-for-logical","slug":"logicode-an-llm-driven-framework-for-logical","title":"LogiCode: an LLM-Driven Framework for Logical Anomaly Detection","date":"2024-06-07","arxiv_id":"2406.04687","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/logicode-an-llm-driven-framework-for-logical#ran","syntology_url":"https://syntology.ai/paper/2406.04687","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.04687"}},"official":{"repos":["22strongestme/LOCO-Annotations"],"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/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/predicting-genetic-mutation-from-whole-slide","slug":"predicting-genetic-mutation-from-whole-slide","title":"Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label Classification","date":"2024-06-05","arxiv_id":"2406.02990","repositories_listed":1,"syntology":null}],"record_sha256":"722868480064ef57b41101023cd0d72f30259fd551539d776fee6122fd3e328f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}