{"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/8","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":8,"pages_in_order":26,"rows_per_page":100,"rows":[701,800],"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/7","next":"/task/binary-classification/papers/9","papers":[{"url":"/paper/bayesian-approximate-kernel-regression-with","slug":"bayesian-approximate-kernel-regression-with","title":"Bayesian Approximate Kernel Regression with Variable Selection","date":"2015-08-05","arxiv_id":"1508.01217","repositories_listed":1,"syntology":null},{"url":"/paper/learning-with-symmetric-label-noise-the","slug":"learning-with-symmetric-label-noise-the","title":"Learning with Symmetric Label Noise: The Importance of Being Unhinged","date":"2015-05-28","arxiv_id":"1505.07634","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-neural-network-for-paraphrase","slug":"convolutional-neural-network-for-paraphrase","title":"Convolutional Neural Network for Paraphrase Identification","date":"2015-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/training-binary-multilayer-neural-networks","slug":"training-binary-multilayer-neural-networks","title":"Training Binary Multilayer Neural Networks for Image Classification using Expectation Backpropagation","date":"2015-03-12","arxiv_id":"1503.03562","repositories_listed":1,"syntology":null},{"url":"/paper/optimally-combining-classifiers-using","slug":"optimally-combining-classifiers-using","title":"Optimally Combining Classifiers Using Unlabeled Data","date":"2015-03-05","arxiv_id":"1503.01811","repositories_listed":1,"syntology":null},{"url":"/paper/on-distinguishability-criteria-for-estimating","slug":"on-distinguishability-criteria-for-estimating","title":"On distinguishability criteria for estimating generative models","date":"2014-12-19","arxiv_id":"1412.6515","repositories_listed":1,"syntology":null},{"url":"/paper/thresholding-classifiers-to-maximize-f1-score","slug":"thresholding-classifiers-to-maximize-f1-score","title":"Thresholding Classifiers to Maximize F1 Score","date":"2014-02-08","arxiv_id":"1402.1892","repositories_listed":1,"syntology":null},{"url":"/paper/polynomial-expansion-of-the-binary","slug":"polynomial-expansion-of-the-binary","title":"Polynomial expansion of the binary classification function","date":"2012-03-26","arxiv_id":"1203.5647","repositories_listed":1,"syntology":null},{"url":"/paper/a-bagging-svm-to-learn-from-positive-and","slug":"a-bagging-svm-to-learn-from-positive-and","title":"A bagging SVM to learn from positive and unlabeled examples","date":"2010-10-05","arxiv_id":"1010.0772","repositories_listed":1,"syntology":null},{"url":"/paper/the-google-similarity-distance","slug":"the-google-similarity-distance","title":"The Google Similarity Distance","date":"2004-12-21","arxiv_id":"cs/0412098","repositories_listed":1,"syntology":null},{"url":null,"slug":"an-automated-classifier-of-harmful-brain","title":"An Automated Classifier of Harmful Brain Activities for Clinical Usage Based on a Vision-Inspired Pre-trained Framework","date":"2025-07-10","arxiv_id":"2507.08874","repositories_listed":0,"syntology":null},{"url":null,"slug":"ddl-a-dataset-for-interpretable-deepfake","title":"DDL: A Dataset for Interpretable Deepfake Detection and Localization in Real-World Scenarios","date":"2025-06-29","arxiv_id":"2506.23292","repositories_listed":0,"syntology":null},{"url":null,"slug":"divide-specialize-and-route-a-new-approach-to","title":"Divide, Specialize, and Route: A New Approach to Efficient Ensemble Learning","date":"2025-06-25","arxiv_id":"2506.20814","repositories_listed":0,"syntology":null},{"url":null,"slug":"private-model-personalization-revisited","title":"Private Model Personalization Revisited","date":"2025-06-24","arxiv_id":"2506.19220","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-strategies-for-personalized","title":"Exploring Strategies for Personalized Radiation Therapy Part I Unlocking Response-Related Tumor Subregions with Class Activation Mapping","date":"2025-06-21","arxiv_id":"2506.17536","repositories_listed":0,"syntology":null},{"url":null,"slug":"i-know-which-llm-wrote-your-code-last-summer","title":"I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution","date":"2025-06-18","arxiv_id":"2506.17323","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-immune-cells-with-label-free-two","title":"Detecting immune cells with label-free two-photon autofluorescence and deep learning","date":"2025-06-17","arxiv_id":"2506.14449","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm-powered-intent-based-categorization-of","title":"LLM-Powered Intent-Based Categorization of Phishing Emails","date":"2025-06-17","arxiv_id":"2506.14337","repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-rates-of-erm-for-agnostic-learning","title":"Universal Rates of ERM for Agnostic Learning","date":"2025-06-17","arxiv_id":"2506.14110","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-existence-of-consistent-adversarial","title":"On the existence of consistent adversarial attacks in high-dimensional linear classification","date":"2025-06-14","arxiv_id":"2506.12454","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-of-bi-directional-gated-loop","title":"Optimization of bi-directional gated loop cell based on multi-head attention mechanism for SSD health state classification model","date":"2025-06-13","arxiv_id":"2506.14830","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-surrogate-risk-bounds-for-binary","title":"Adversarial Surrogate Risk Bounds for Binary Classification","date":"2025-06-11","arxiv_id":"2506.09348","repositories_listed":0,"syntology":null},{"url":null,"slug":"deal-disentangling-transformer-head","title":"DEAL: Disentangling Transformer Head Activations for LLM Steering","date":"2025-06-10","arxiv_id":"2506.08359","repositories_listed":0,"syntology":null},{"url":"/paper/dison-decentralized-isolation-networks-for","slug":"dison-decentralized-isolation-networks-for","title":"DIsoN: Decentralized Isolation Networks for Out-of-Distribution Detection in Medical Imaging","date":"2025-06-10","arxiv_id":"2506.09024","repositories_listed":0,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dison-decentralized-isolation-networks-for#ran","syntology_url":"https://syntology.ai/paper/2506.09024","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.09024"}},"official":null}},{"url":null,"slug":"chancery-evaluating-corporate-governance","title":"CHANCERY: Evaluating Corporate Governance Reasoning Capabilities in Language Models","date":"2025-06-05","arxiv_id":"2506.04636","repositories_listed":0,"syntology":null},{"url":"/paper/zeroth-order-optimization-finds-flat-minima","slug":"zeroth-order-optimization-finds-flat-minima","title":"Zeroth-Order Optimization Finds Flat Minima","date":"2025-06-05","arxiv_id":"2506.05454","repositories_listed":0,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":3,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 3 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/zeroth-order-optimization-finds-flat-minima#ran","syntology_url":"https://syntology.ai/paper/2506.05454","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.05454"}},"official":null}},{"url":null,"slug":"does-prompt-design-impact-quality-of-data","title":"Does Prompt Design Impact Quality of Data Imputation by LLMs?","date":"2025-06-04","arxiv_id":"2506.04172","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-ensembling-methods-for-healthcare-and","title":"Quantum Ensembling Methods for Healthcare and Life Science","date":"2025-06-02","arxiv_id":"2506.02213","repositories_listed":0,"syntology":null},{"url":null,"slug":"trade-offs-in-data-memorization-via-strong","title":"Trade-offs in Data Memorization via Strong Data Processing Inequalities","date":"2025-06-02","arxiv_id":"2506.01855","repositories_listed":0,"syntology":null},{"url":null,"slug":"localized-forest-fire-risk-prediction-a","title":"Localized Forest Fire Risk Prediction: A Department-Aware Approach for Operational Decision Support","date":"2025-06-01","arxiv_id":"2506.04254","repositories_listed":0,"syntology":null},{"url":null,"slug":"hidden-persuasion-detecting-manipulative","title":"Hidden Persuasion: Detecting Manipulative Narratives on Social Media During the 2022 Russian Invasion of Ukraine","date":"2025-05-29","arxiv_id":"2505.24028","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-rich-and-the-simple-on-the-implicit-bias","title":"The Rich and the Simple: On the Implicit Bias of Adam and SGD","date":"2025-05-29","arxiv_id":"2505.24022","repositories_listed":0,"syntology":null},{"url":null,"slug":"individualised-counterfactual-examples-using","title":"Individualised Counterfactual Examples Using Conformal Prediction Intervals","date":"2025-05-28","arxiv_id":"2505.22326","repositories_listed":0,"syntology":null},{"url":null,"slug":"absolutenet-a-deep-learning-neural-network-to","title":"AbsoluteNet: A Deep Learning Neural Network to Classify Cerebral Hemodynamic Responses of Auditory Processing","date":"2025-05-27","arxiv_id":"2506.00039","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-cascaded-binary-classification-and","title":"Leveraging Cascaded Binary Classification and Multimodal Fusion for Dementia Detection through Spontaneous Speech","date":"2025-05-26","arxiv_id":"2505.19446","repositories_listed":0,"syntology":null},{"url":null,"slug":"revolutionizing-wildfire-detection-with","title":"Revolutionizing Wildfire Detection with Convolutional Neural Networks: A VGG16 Model Approach","date":"2025-05-26","arxiv_id":"2505.19479","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-smart-healthcare-system-for-monkeypox-skin","title":"A Smart Healthcare System for Monkeypox Skin Lesion Detection and Tracking","date":"2025-05-25","arxiv_id":"2505.19023","repositories_listed":0,"syntology":null},{"url":null,"slug":"debate-to-detect-reformulating-misinformation","title":"Debate-to-Detect: Reformulating Misinformation Detection as a Real-World Debate with Large Language Models","date":"2025-05-24","arxiv_id":"2505.18596","repositories_listed":0,"syntology":null},{"url":null,"slug":"mechanical-in-sensor-computing-a-programmable","title":"Mechanical in-sensor computing: a programmable meta-sensor for structural damage classification without external electronic power","date":"2025-05-24","arxiv_id":"2505.18579","repositories_listed":0,"syntology":null},{"url":null,"slug":"anatomy-guided-multitask-learning-for-mri","title":"Anatomy-Guided Multitask Learning for MRI-Based Classification of Placenta Accreta Spectrum and its Subtypes","date":"2025-05-23","arxiv_id":"2505.17484","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-with-trained-embeddings-provably","title":"Attention with Trained Embeddings Provably Selects Important Tokens","date":"2025-05-22","arxiv_id":"2505.17282","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-classification-enhancement-and","title":"Self-Classification Enhancement and Correction for Weakly Supervised Object Detection","date":"2025-05-22","arxiv_id":"2505.16294","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-estimation-and-learning-under","title":"Adaptive Estimation and Learning under Temporal Distribution Shift","date":"2025-05-21","arxiv_id":"2505.15803","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-boost-via-optimal-retraining-an-analysis","title":"Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing","date":"2025-05-21","arxiv_id":"2505.15195","repositories_listed":0,"syntology":null},{"url":null,"slug":"csagc-ids-a-dual-module-deep-learning-network","title":"CSAGC-IDS: A Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data","date":"2025-05-20","arxiv_id":"2505.14027","repositories_listed":0,"syntology":null},{"url":null,"slug":"safety-alignment-can-be-not-superficial-with","title":"Safety Alignment Can Be Not Superficial With Explicit Safety Signals","date":"2025-05-19","arxiv_id":"2505.17072","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11110","title":"Deepfake Forensic Analysis: Source Dataset Attribution and Legal Implications of Synthetic Media Manipulation","date":"2025-05-16","arxiv_id":"2505.11110","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-effective-flow-based-method-for-positive","title":"An Effective Flow-based Method for Positive-Unlabeled Learning: 2-HNC","date":"2025-05-13","arxiv_id":"2505.08212","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-scene-analysis-using-deep-learning","title":"Crowd Scene Analysis using Deep Learning Techniques","date":"2025-05-13","arxiv_id":"2505.08834","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-fast-rates-of-convergence-for-neural","title":"Super-fast rates of convergence for Neural Networks Classifiers under the Hard Margin Condition","date":"2025-05-13","arxiv_id":"2505.08262","repositories_listed":0,"syntology":null},{"url":null,"slug":"incomplete-in-context-learning","title":"Incomplete In-context Learning","date":"2025-05-12","arxiv_id":"2505.07251","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-for-multi-class-image","title":"Active Learning for Multi-class Image Classification","date":"2025-05-11","arxiv_id":"2505.06825","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-bias-exploration-and-mitigation","title":"Fine-Grained Bias Exploration and Mitigation for Group-Robust Classification","date":"2025-05-11","arxiv_id":"2505.06831","repositories_listed":0,"syntology":null},{"url":null,"slug":"markmatch-same-hand-stuffing-detection","title":"MarkMatch: Same-Hand Stuffing Detection","date":"2025-05-11","arxiv_id":"2505.07032","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-distracting-effect-understanding","title":"The Distracting Effect: Understanding Irrelevant Passages in RAG","date":"2025-05-11","arxiv_id":"2505.06914","repositories_listed":0,"syntology":null},{"url":null,"slug":"voice-biomarkers-of-perinatal-depression","title":"Voice biomarkers of perinatal depression: cross-sectional nationwide pilot study report","date":"2025-05-09","arxiv_id":"2505.06412","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-context-learning-for-label-efficient","title":"In-Context Learning for Label-Efficient Cancer Image Classification in Oncology","date":"2025-05-08","arxiv_id":"2505.08798","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-estimation-in-binary","title":"Performance Estimation in Binary Classification Using Calibrated Confidence","date":"2025-05-08","arxiv_id":"2505.05295","repositories_listed":0,"syntology":null},{"url":null,"slug":"multitask-lstm-for-arboviral-outbreak","title":"Multitask LSTM for Arboviral Outbreak Prediction Using Public Health Data","date":"2025-05-07","arxiv_id":"2505.04566","repositories_listed":0,"syntology":null},{"url":null,"slug":"pac-bayesian-risk-bounds-for-fully-connected","title":"PAC-Bayesian risk bounds for fully connected deep neural network with Gaussian priors","date":"2025-05-07","arxiv_id":"2505.04341","repositories_listed":0,"syntology":null},{"url":null,"slug":"event-triggered-gat-lstm-framework-for-attack","title":"Event-Triggered GAT-LSTM Framework for Attack Detection in Heating, Ventilation, and Air Conditioning Systems","date":"2025-05-06","arxiv_id":"2505.03559","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-omics-based-classification-the-role","title":"Improving Omics-Based Classification: The Role of Feature Selection and Synthetic Data Generation","date":"2025-05-06","arxiv_id":"2505.03387","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-approach-for-depressive","title":"A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos","date":"2025-05-05","arxiv_id":"2505.03845","repositories_listed":0,"syntology":null},{"url":null,"slug":"eye-movements-as-indicators-of-deception-a","title":"Eye Movements as Indicators of Deception: A Machine Learning Approach","date":"2025-05-05","arxiv_id":"2505.02649","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-self-supervised-transformer-for-unusable","title":"A Self-Supervised Transformer for Unusable Shared Bike Detection","date":"2025-05-02","arxiv_id":"2505.00932","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-learn-a-star-binary-classification","title":"How to Learn a Star: Binary Classification with Starshaped Polyhedral Sets","date":"2025-05-02","arxiv_id":"2505.01346","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-double-descent-behavior-in-two-layer","title":"The Double Descent Behavior in Two Layer Neural Network for Binary Classification","date":"2025-04-27","arxiv_id":"2504.19351","repositories_listed":0,"syntology":null},{"url":null,"slug":"subject-independent-classification-of","title":"Subject-independent Classification of Meditative State from the Resting State using EEG","date":"2025-04-25","arxiv_id":"2504.18095","repositories_listed":0,"syntology":null},{"url":null,"slug":"intelligent-detection-of-non-essential-iot","title":"Intelligent Detection of Non-Essential IoT Traffic on the Home Gateway","date":"2025-04-22","arxiv_id":"2504.18571","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-selection-for-erms","title":"Data Selection for ERMs","date":"2025-04-20","arxiv_id":"2504.14572","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-baseline-for-self-state-identification-and","title":"A Baseline for Self-state Identification and Classification in Mental Health Data: CLPsych 2025 Task","date":"2025-04-18","arxiv_id":"2504.14066","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-achieve-higher-accuracy-with-less","title":"How to Achieve Higher Accuracy with Less Training Points?","date":"2025-04-18","arxiv_id":"2504.13586","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-bvd-re-emergence-in-irish-cattle","title":"Predicting BVD Re-emergence in Irish Cattle From Highly Imbalanced Herd-Level Data Using Machine Learning Algorithms","date":"2025-04-17","arxiv_id":"2504.13116","repositories_listed":0,"syntology":null},{"url":null,"slug":"wearable-derived-behavioral-and-physiological","title":"Wearable-Derived Behavioral and Physiological Biomarkers for Classifying Unipolar and Bipolar Depression Severity","date":"2025-04-17","arxiv_id":"2504.13331","repositories_listed":0,"syntology":null},{"url":null,"slug":"decision-based-ai-visual-navigation-for","title":"Decision-based AI Visual Navigation for Cardiac Ultrasounds","date":"2025-04-16","arxiv_id":"2504.12535","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-is-task-vector-provably-effective-for","title":"When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers","date":"2025-04-15","arxiv_id":"2504.10957","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-worst-case-online-classification-vc","title":"Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks","date":"2025-04-14","arxiv_id":"2504.10598","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-cybersecurity-incidents-using","title":"Investigating cybersecurity incidents using large language models in latest-generation wireless networks","date":"2025-04-14","arxiv_id":"2504.13196","repositories_listed":0,"syntology":null},{"url":null,"slug":"who-is-more-bayesian-humans-or-chatgpt","title":"Who is More Bayesian: Humans or ChatGPT?","date":"2025-04-14","arxiv_id":"2504.10636","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-evaluating-local-llms-rethinking","title":"Meta-Evaluating Local LLMs: Rethinking Performance Metrics for Serious Games","date":"2025-04-13","arxiv_id":"2504.12333","repositories_listed":0,"syntology":null},{"url":null,"slug":"diabetic-retinopathy-detection-based-on","title":"Diabetic Retinopathy Detection Based on Convolutional Neural Networks with SMOTE and CLAHE Techniques Applied to Fundus Images","date":"2025-04-08","arxiv_id":"2504.05696","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-consequentialist-critique-of-binary","title":"A Consequentialist Critique of Binary Classification Evaluation Practices","date":"2025-04-06","arxiv_id":"2504.04528","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-load-dependent-decision-referrals-for","title":"Task load dependent decision referrals for joint binary classification in human-automation teams","date":"2025-04-05","arxiv_id":"2504.04248","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-geometry-of-receiver-operating","title":"On the Geometry of Receiver Operating Characteristic and Precision-Recall Curves","date":"2025-04-02","arxiv_id":"2504.02169","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-assisted-machine-learning-models-for","title":"Quantum-Assisted Machine Learning Models for Enhanced Weather Prediction","date":"2025-03-30","arxiv_id":"2503.23408","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-models-applied-to-skin-and-oral","title":"Diffusion models applied to skin and oral cancer classification","date":"2025-03-28","arxiv_id":"2504.00026","repositories_listed":0,"syntology":null},{"url":null,"slug":"extremely-simple-out-of-distribution","title":"Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning","date":"2025-03-28","arxiv_id":"2503.22197","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-monotonic-attention-based-read-write","title":"Non-Monotonic Attention-based Read/Write Policy Learning for Simultaneous Translation","date":"2025-03-28","arxiv_id":"2503.22051","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-analysis-of-image-video-and-audio","title":"Comparative Analysis of Image, Video, and Audio Classifiers for Automated News Video Segmentation","date":"2025-03-27","arxiv_id":"2503.21848","repositories_listed":0,"syntology":null},{"url":null,"slug":"fakereasoning-towards-generalizable-forgery","title":"FakeReasoning: Towards Generalizable Forgery Detection and Reasoning","date":"2025-03-27","arxiv_id":"2503.21210","repositories_listed":0,"syntology":null},{"url":null,"slug":"harnessing-mixed-features-for-imbalance-data","title":"Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring","date":"2025-03-26","arxiv_id":"2503.22730","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-approaches-for-blood-disease","title":"Deep Learning Approaches for Blood Disease Diagnosis Across Hematopoietic Lineages","date":"2025-03-25","arxiv_id":"2503.20049","repositories_listed":0,"syntology":null},{"url":null,"slug":"post-hoc-calibrated-anomaly-detection","title":"Post-Hoc Calibrated Anomaly Detection","date":"2025-03-25","arxiv_id":"2503.19577","repositories_listed":0,"syntology":null},{"url":null,"slug":"prectr-a-synergistic-framework-for","title":"PRECTR: A Synergistic Framework for Integrating Personalized Search Relevance Matching and CTR Prediction","date":"2025-03-24","arxiv_id":"2503.18395","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-diagnosis-of-lung-diseases-using","title":"Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification","date":"2025-03-22","arxiv_id":"2503.18973","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-consistency-and-reproducibility-in","title":"Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Tasks","date":"2025-03-21","arxiv_id":"2503.16974","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-optimal-neural-feedback-control-of","title":"Time-optimal neural feedback control of nilpotent systems as a binary classification problem","date":"2025-03-21","arxiv_id":"2503.17581","repositories_listed":0,"syntology":null},{"url":null,"slug":"patch-based-learning-of-adaptive-total","title":"Patch-based learning of adaptive Total Variation parameter maps for blind image denoising","date":"2025-03-20","arxiv_id":"2503.16010","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-quantum-svm-training-on-ising","title":"Probabilistic Quantum SVM Training on Ising Machine","date":"2025-03-20","arxiv_id":"2503.16363","repositories_listed":0,"syntology":null},{"url":null,"slug":"truthlens-explainable-deepfake-detection-for","title":"TruthLens: Explainable DeepFake Detection for Face Manipulated and Fully Synthetic Data","date":"2025-03-20","arxiv_id":"2503.15867","repositories_listed":0,"syntology":null}],"record_sha256":"fd9d5ecfabb628bc6f1970eb1e06418ee106938dc1439b4bcd8005e471b977da","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}