{"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":"/method/logistic-regression/papers/13","list_of":"/method/logistic-regression","method":"Logistic Regression","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":13,"pages_in_order":19,"rows_per_page":100,"rows":[1201,1300],"of":1886,"counts":{"archive_papers_tagged":1886,"with_a_code_link":461,"where_syntology_ran_a_sample":62,"not_listed_spam_title":0,"listed":1886,"listed_where_code_ran":62,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":48,"every_run_a_failure_of_syntologys_instrument":14,"listed_with_a_run_with_no_instrument_failure":48,"listed_every_run_a_failure_of_syntologys_instrument":14,"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":"/method/logistic-regression","prev":"/method/logistic-regression/papers/12","next":"/method/logistic-regression/papers/14","papers":[{"paper":null,"slug":"an-isolation-forest-learning-based-outlier","title":"An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies","date":"2020-12-09","arxiv_id":"2101.03141","n_code_links":0,"syntology":null},{"paper":null,"slug":"facial-expressions-can-detect-parkinson-s","title":"Facial expressions can detect Parkinson's disease: preliminary evidence from videos collected online","date":"2020-12-09","arxiv_id":"2012.05373","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-individual-substance-abuse","title":"Predicting Individual Substance Abuse Vulnerability using Machine Learning Techniques","date":"2020-12-09","arxiv_id":"2101.03184","n_code_links":0,"syntology":null},{"paper":null,"slug":"discourse-parsing-of-contentious-non","title":"Discourse Parsing of Contentious, Non-Convergent Online Discussions","date":"2020-12-08","arxiv_id":"2012.04585","n_code_links":0,"syntology":null},{"paper":null,"slug":"computing-flood-probabilities-using-twitter","title":"Computing flood probabilities using Twitter: application to the Houston urban area during Harvey","date":"2020-12-07","arxiv_id":"2012.03731","n_code_links":0,"syntology":null},{"paper":null,"slug":"interpretability-and-explainability-a-machine","title":"Interpretability and Explainability: A Machine Learning Zoo Mini-tour","date":"2020-12-03","arxiv_id":"2012.01805","n_code_links":0,"syntology":null},{"paper":"/paper/sample-efficient-l0-l2-constrained-structure","slug":"sample-efficient-l0-l2-constrained-structure","title":"Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models","date":"2020-12-03","arxiv_id":"2012.01744","n_code_links":1,"syntology":null},{"paper":null,"slug":"traffic-surveillance-using-vehicle-license","title":"Traffic Surveillance using Vehicle License Plate Detection and Recognition in Bangladesh","date":"2020-12-03","arxiv_id":"2012.02218","n_code_links":0,"syntology":null},{"paper":null,"slug":"covid-19-cough-classification-using-machine","title":"COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings","date":"2020-12-02","arxiv_id":"2012.01926","n_code_links":0,"syntology":null},{"paper":"/paper/learning-universal-shape-dictionary-for","slug":"learning-universal-shape-dictionary-for","title":"Learning Universal Shape Dictionary for Realtime Instance Segmentation","date":"2020-12-02","arxiv_id":"2012.01050","n_code_links":1,"syntology":null},{"paper":null,"slug":"bennettnlp-at-semeval-2020-task-8-multimodal","title":"BennettNLP at SemEval-2020 Task 8: Multimodal sentiment classification Using Hybrid Hierarchical Classifier","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"coli-at-uds-at-semeval-2020-task-12-offensive","title":"CoLi at UdS at SemEval-2020 Task 12: Offensive Tweet Detection with Ensembling","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/confluence-a-robust-non-iou-alternative-to","slug":"confluence-a-robust-non-iou-alternative-to","title":"Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection","date":"2020-12-01","arxiv_id":"2012.00257","n_code_links":3,"syntology":null},{"paper":null,"slug":"interpretable-phase-detection-and","title":"Interpretable Phase Detection and Classification with Persistent Homology","date":"2020-12-01","arxiv_id":"2012.00783","n_code_links":0,"syntology":null},{"paper":null,"slug":"irlab-daiict-at-semeval-2020-task-12-machine","title":"IRLab\\_DAIICT at SemEval-2020 Task 12: Machine Learning and Deep Learning Methods for Offensive Language Identification","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"irlab-daiict-at-semeval-2020-task-9-machine","title":"IRLab\\_DAIICT at SemEval-2020 Task 9: Machine Learning and Deep Learning Methods for Sentiment Analysis of Code-Mixed Tweets","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"nova-wang-at-semeval-2020-task-12","title":"Nova-Wang at SemEval-2020 Task 12: OffensEmblert: An Ensemble ofOffensive Language Classifiers","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/optimal-visual-search-based-on-a-model-of","slug":"optimal-visual-search-based-on-a-model-of","title":"Optimal visual search based on a model of target detectability in natural images","date":"2020-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"so-at-semeval-2020-task-7-deeppavlov-logistic","title":"SO at SemEval-2020 Task 7: DeepPavlov Logistic Regression with BERT Embeddings vs SVR at Funniness Evaluation","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"tuemix-at-semeval-2020-task-9-logistic","title":"TueMix at SemEval-2020 Task 9: Logistic Regression with Linguistic Feature Set","date":"2020-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"a-role-for-prior-knowledge-in-statistical","title":"A Role for Prior Knowledge in Statistical Classification of the Transition from MCI to Alzheimer's Disease","date":"2020-11-28","arxiv_id":"2012.00538","n_code_links":0,"syntology":null},{"paper":"/paper/learning-from-incomplete-data-by-simultaneous","slug":"learning-from-incomplete-data-by-simultaneous","title":"Learning from Incomplete Features by Simultaneous Training of Neural Networks and Sparse Coding","date":"2020-11-28","arxiv_id":"2011.14047","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-detection-of-cardiac-chambers-using","title":"Automatic Detection of Cardiac Chambers Using an Attention-based YOLOv4 Framework from Four-chamber View of Fetal Echocardiography","date":"2020-11-26","arxiv_id":"2011.13096","n_code_links":0,"syntology":null},{"paper":null,"slug":"classification-supporting-covid-19","title":"Classification supporting COVID-19 diagnostics based on patient survey data","date":"2020-11-24","arxiv_id":"2011.12247","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-robust-and-generalizable-transcriptomic","title":"A robust and generalizable immune-relatedsignature for sepsis diagnostics","date":"2020-11-23","arxiv_id":"2011.11343","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-system-for-automatic-rice-disease","title":"A System for Automatic Rice Disease Detection from Rice Paddy Images Serviced via a Chatbot","date":"2020-11-21","arxiv_id":"2011.10823","n_code_links":0,"syntology":null},{"paper":"/paper/optimizing-approximate-leave-one-out-cross","slug":"optimizing-approximate-leave-one-out-cross","title":"Optimizing Approximate Leave-one-out Cross-validation to Tune Hyperparameters","date":"2020-11-20","arxiv_id":"2011.10218","n_code_links":2,"syntology":null},{"paper":null,"slug":"anderson-acceleration-of-coordinate-descent","title":"Anderson acceleration of coordinate descent","date":"2020-11-19","arxiv_id":"2011.10065","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparative-approach-on-detecting-multi","title":"A comparative approach on detecting multi-lingual and multi-oriented text in natural scene images","date":"2020-11-17","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-number-fields","title":"Machine-Learning Number Fields","date":"2020-11-17","arxiv_id":"2011.08958","n_code_links":0,"syntology":null},{"paper":null,"slug":"avoiding-communication-in-logistic-regression","title":"Avoiding Communication in Logistic Regression","date":"2020-11-16","arxiv_id":"2011.08281","n_code_links":0,"syntology":null},{"paper":null,"slug":"frdet-balanced-and-lightweight-object","title":"FRDet: Balanced and Lightweight Object Detector based on Fire-Residual Modules for Embedded Processor of Autonomous Driving","date":"2020-11-16","arxiv_id":"2011.08061","n_code_links":0,"syntology":null},{"paper":"/paper/scaled-yolov4-scaling-cross-stage-partial","slug":"scaled-yolov4-scaling-cross-stage-partial","title":"Scaled-YOLOv4: Scaling Cross Stage Partial Network","date":"2020-11-16","arxiv_id":"2011.08036","n_code_links":41,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":{"repos":["WongKinYiu/ScaledYOLOv4"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"paper":null,"slug":"explaining-the-adaptive-generalisation-gap","title":"A Random Matrix Theory Approach to Damping in Deep Learning","date":"2020-11-15","arxiv_id":"2011.08181","n_code_links":0,"syntology":null},{"paper":"/paper/real-time-polyp-detection-localisation-and","slug":"real-time-polyp-detection-localisation-and","title":"Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning","date":"2020-11-15","arxiv_id":"2011.07631","n_code_links":1,"syntology":null},{"paper":null,"slug":"using-convolutional-variational-autoencoders","title":"Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data","date":"2020-11-14","arxiv_id":"2011.07406","n_code_links":0,"syntology":null},{"paper":null,"slug":"sparse-representations-of-positive-functions","title":"Sparse Representations of Positive Functions via First and Second-Order Pseudo-Mirror Descent","date":"2020-11-13","arxiv_id":"2011.07142","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-machine-learning-to-calibrate-storm","title":"Using Machine Learning to Calibrate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System","date":"2020-11-12","arxiv_id":"2012.00679","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-decision-making-tool-to-fine-tune-abnormal","title":"A decision-making tool to fine-tune abnormal levels in the complete blood count tests","date":"2020-11-11","arxiv_id":"2011.05900","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-improved-helmet-detection-method-for","title":"An improved helmet detection method for YOLOv3 on an unbalanced dataset","date":"2020-11-09","arxiv_id":"2011.04214","n_code_links":0,"syntology":null},{"paper":"/paper/real-time-object-detection-method-based-on","slug":"real-time-object-detection-method-based-on","title":"Real-time object detection method based on improved YOLOv4-tiny","date":"2020-11-09","arxiv_id":"2011.04244","n_code_links":1,"syntology":null},{"paper":"/paper/fighting-an-infodemic-covid-19-fake-news","slug":"fighting-an-infodemic-covid-19-fake-news","title":"Fighting an Infodemic: COVID-19 Fake News Dataset","date":"2020-11-06","arxiv_id":"2011.03327","n_code_links":2,"syntology":null},{"paper":null,"slug":"qmul-sds-diacr-ita2020-evaluating","title":"QMUL-SDS @ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian","date":"2020-11-05","arxiv_id":"2011.02935","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-scalable-approach-for-privacy-preserving","title":"A Scalable Approach for Privacy-Preserving Collaborative Machine Learning","date":"2020-11-03","arxiv_id":"2011.01963","n_code_links":0,"syntology":null},{"paper":null,"slug":"sgb-stochastic-gradient-bound-method-for","title":"SGB: Stochastic Gradient Bound Method for Optimizing Partition Functions","date":"2020-11-03","arxiv_id":"2011.01474","n_code_links":0,"syntology":null},{"paper":null,"slug":"differentially-private-bayesian-inference-for-1","title":"Differentially Private Bayesian Inference for Generalized Linear Models","date":"2020-11-01","arxiv_id":"2011.00467","n_code_links":0,"syntology":null},{"paper":null,"slug":"short-text-classification-approach-to","title":"Short Text Classification Approach to Identify Child Sexual Exploitation Material","date":"2020-10-29","arxiv_id":"2011.01113","n_code_links":0,"syntology":null},{"paper":null,"slug":"handgun-detection-using-combined-human-pose","title":"Handgun detection using combined human pose and weapon appearance","date":"2020-10-26","arxiv_id":"2010.13753","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-a-binary-classification-model-to","title":"Using a Binary Classification Model to Predict the Likelihood of Enrolment to the Undergraduate Program of a Philippine University","date":"2020-10-26","arxiv_id":"2010.15601","n_code_links":0,"syntology":null},{"paper":null,"slug":"statistical-optimality-and-stability-of","title":"Statistical optimality and stability of tangent transform algorithms in logit models","date":"2020-10-25","arxiv_id":"2010.13039","n_code_links":0,"syntology":null},{"paper":null,"slug":"road-accident-proneness-indicator-based-on","title":"Road Accident Proneness Indicator Based On Time, Weather And Location Specificity Using Graph Neural Networks","date":"2020-10-24","arxiv_id":"2010.12953","n_code_links":0,"syntology":null},{"paper":null,"slug":"variational-bayesian-unlearning","title":"Variational Bayesian Unlearning","date":"2020-10-24","arxiv_id":"2010.12883","n_code_links":0,"syntology":null},{"paper":"/paper/accelerating-metropolis-hastings-with","slug":"accelerating-metropolis-hastings-with","title":"Accelerating Metropolis-Hastings with Lightweight Inference Compilation","date":"2020-10-23","arxiv_id":"2010.12128","n_code_links":1,"syntology":null},{"paper":"/paper/approxdet-content-and-contention-aware","slug":"approxdet-content-and-contention-aware","title":"ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles","date":"2020-10-21","arxiv_id":"2010.10754","n_code_links":1,"syntology":null},{"paper":null,"slug":"detection-of-covid-19-through-the-analysis-of","title":"Detection of COVID-19 through the analysis of vocal fold oscillations","date":"2020-10-21","arxiv_id":"2010.10707","n_code_links":0,"syntology":null},{"paper":"/paper/feature-inference-attack-on-model-predictions","slug":"feature-inference-attack-on-model-predictions","title":"Feature Inference Attack on Model Predictions in Vertical Federated Learning","date":"2020-10-20","arxiv_id":"2010.10152","n_code_links":1,"syntology":null},{"paper":null,"slug":"concentration-of-solutions-to-random","title":"A Concentration of Measure Framework to study convex problems and other implicit formulation problems in machine learning","date":"2020-10-19","arxiv_id":"2010.09877","n_code_links":0,"syntology":null},{"paper":null,"slug":"binary-choice-with-asymmetric-loss-in-a-data","title":"Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice","date":"2020-10-16","arxiv_id":"2010.08463","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-theory-of-hyperbolic-prototype-learning","title":"A Theory of Hyperbolic Prototype Learning","date":"2020-10-15","arxiv_id":"2010.07744","n_code_links":0,"syntology":null},{"paper":null,"slug":"chatbot-interaction-with-artificial","title":"Chatbot Interaction with Artificial Intelligence: Human Data Augmentation with T5 and Language Transformer Ensemble for Text Classification","date":"2020-10-12","arxiv_id":"2010.05990","n_code_links":0,"syntology":null},{"paper":null,"slug":"differentially-private-secure-multi-party","title":"Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications","date":"2020-10-12","arxiv_id":"2010.05867","n_code_links":0,"syntology":null},{"paper":null,"slug":"online-learning-and-distributed-control-for","title":"Online Learning and Distributed Control for Residential Demand Response","date":"2020-10-11","arxiv_id":"2010.05153","n_code_links":0,"syntology":null},{"paper":null,"slug":"cryptocredit-securely-training-fair-models","title":"CryptoCredit: Securely Training Fair Models","date":"2020-10-09","arxiv_id":"2010.04840","n_code_links":0,"syntology":null},{"paper":null,"slug":"identifying-risk-of-opioid-use-disorder-for","title":"Identifying Risk of Opioid Use Disorder for Patients Taking Opioid Medications with Deep Learning","date":"2020-10-09","arxiv_id":"2010.04589","n_code_links":0,"syntology":null},{"paper":null,"slug":"long-distance-tiny-face-detection-based-on","title":"Long-distance tiny face detection based on enhanced YOLOv3 for unmanned system","date":"2020-10-09","arxiv_id":"2010.04421","n_code_links":0,"syntology":null},{"paper":null,"slug":"sparse-network-asymptotics-for-logistic","title":"Sparse network asymptotics for logistic regression","date":"2020-10-09","arxiv_id":"2010.04703","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-subcarrier-parameter-and-power","title":"Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels","date":"2020-10-08","arxiv_id":"2010.04061","n_code_links":0,"syntology":null},{"paper":"/paper/theedhum-nandrum-dravidian-codemix-fire2020","slug":"theedhum-nandrum-dravidian-codemix-fire2020","title":"Theedhum Nandrum@Dravidian-CodeMix-FIRE2020: A Sentiment Polarity Classifier for YouTube Comments with Code-switching between Tamil, Malayalam and English","date":"2020-10-07","arxiv_id":"2010.03189","n_code_links":1,"syntology":null},{"paper":null,"slug":"yodar-uncertainty-based-sensor-fusion-for","title":"YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors","date":"2020-10-07","arxiv_id":"2010.03320","n_code_links":0,"syntology":null},{"paper":"/paper/cross-lingual-text-classification-with","slug":"cross-lingual-text-classification-with","title":"Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher","date":"2020-10-06","arxiv_id":"2010.02562","n_code_links":1,"syntology":null},{"paper":"/paper/metadetect-uncertainty-quantification-and","slug":"metadetect-uncertainty-quantification-and","title":"MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection","date":"2020-10-04","arxiv_id":"2010.01695","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluating-progress-on-machine-learning-for","title":"Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data","date":"2020-10-02","arxiv_id":"2010.01149","n_code_links":0,"syntology":null},{"paper":null,"slug":"fake-news-spreader-detection-on-twitter-using","title":"Fake News Spreader Detection on Twitter using Character N-Grams. Notebook for PAN at CLEF 2020","date":"2020-09-29","arxiv_id":"2009.13859","n_code_links":0,"syntology":null},{"paper":"/paper/detecting-soccer-balls-with-reduced-neural","slug":"detecting-soccer-balls-with-reduced-neural","title":"Detecting soccer balls with reduced neural networks: a comparison of multiple architectures under constrained hardware scenarios","date":"2020-09-28","arxiv_id":"2009.13684","n_code_links":1,"syntology":null},{"paper":"/paper/transparency-auditability-and-explainability","slug":"transparency-auditability-and-explainability","title":"Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring","date":"2020-09-28","arxiv_id":"2009.13384","n_code_links":1,"syntology":null},{"paper":"/paper/a-little-goes-a-long-way-improving-toxic","slug":"a-little-goes-a-long-way-improving-toxic","title":"A little goes a long way: Improving toxic language classification despite data scarcity","date":"2020-09-25","arxiv_id":"2009.12344","n_code_links":1,"syntology":null},{"paper":"/paper/exploiting-vietnamese-social-media","slug":"exploiting-vietnamese-social-media","title":"Exploiting Vietnamese Social Media Characteristics for Textual Emotion Recognition in Vietnamese","date":"2020-09-23","arxiv_id":"2009.11005","n_code_links":0,"syntology":null},{"paper":null,"slug":"transient-classification-in-low-snr","title":"Transient Classification in low SNR Gravitational Wave data using Deep Learning","date":"2020-09-20","arxiv_id":"2009.12168","n_code_links":0,"syntology":null},{"paper":null,"slug":"bid-shading-by-win-rate-estimation-and","title":"Bid Shading by Win-Rate Estimation and Surplus Maximization","date":"2020-09-19","arxiv_id":"2009.09259","n_code_links":0,"syntology":null},{"paper":"/paper/rcnn-for-region-of-interest-detection-in","slug":"rcnn-for-region-of-interest-detection-in","title":"RCNN for Region of Interest Detection in Whole Slide Images","date":"2020-09-16","arxiv_id":"2009.07532","n_code_links":1,"syntology":null},{"paper":null,"slug":"using-machine-learning-and-alternative-data","title":"Using Machine Learning and Alternative Data to Predict Movements in Market Risk","date":"2020-09-16","arxiv_id":"2009.07947","n_code_links":0,"syntology":null},{"paper":"/paper/a-mobile-app-for-wound-localization-using","slug":"a-mobile-app-for-wound-localization-using","title":"A Mobile App for Wound Localization using Deep Learning","date":"2020-09-15","arxiv_id":"2009.07133","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-vertical-federated-learning-method-for","title":"A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoring","date":"2020-09-14","arxiv_id":"2009.06218","n_code_links":0,"syntology":null},{"paper":null,"slug":"fairness-constraints-in-semi-supervised","title":"Fairness Constraints in Semi-supervised Learning","date":"2020-09-14","arxiv_id":"2009.06190","n_code_links":0,"syntology":null},{"paper":null,"slug":"spinopelvic-anatomic-parameters-prediction","title":"Spinopelvic Anatomic Parameters Prediction Model of NSLBP based on data mining","date":"2020-09-14","arxiv_id":"2009.10609","n_code_links":0,"syntology":null},{"paper":"/paper/yolobile-real-time-object-detection-on-mobile","slug":"yolobile-real-time-object-detection-on-mobile","title":"YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design","date":"2020-09-12","arxiv_id":"2009.05697","n_code_links":3,"syntology":null},{"paper":"/paper/trex-tree-ensemble-representer-point","slug":"trex-tree-ensemble-representer-point","title":"TREX: Tree-Ensemble Representer-Point Explanations","date":"2020-09-11","arxiv_id":"2009.05530","n_code_links":1,"syntology":null},{"paper":null,"slug":"developing-and-improving-risk-models-using","title":"Developing and Improving Risk Models using Machine-learning Based Algorithms","date":"2020-09-09","arxiv_id":"2009.04559","n_code_links":0,"syntology":null},{"paper":null,"slug":"health-behaviors-associated-with-the-growing","title":"Health-behaviors associated with the growing risk of adolescent suicide attempts: A data-driven cross-sectional study","date":"2020-09-08","arxiv_id":"2009.03966","n_code_links":0,"syntology":null},{"paper":"/paper/stochastic-yolo-efficient-probabilistic","slug":"stochastic-yolo-efficient-probabilistic","title":"Stochastic-YOLO: Efficient Probabilistic Object Detection under Dataset Shifts","date":"2020-09-07","arxiv_id":"2009.02967","n_code_links":1,"syntology":{"ran":8,"of":12,"n_ran_checked":7,"n_instrument":1,"unverified":4,"pointer_only":5,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["tjiagom/stochastic-yolo"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"the-integrity-of-machine-learning-algorithms","title":"The Integrity of Machine Learning Algorithms against Software Defect Prediction","date":"2020-09-05","arxiv_id":"2009.02571","n_code_links":0,"syntology":null},{"paper":null,"slug":"fairxgboost-fairness-aware-classification-in","title":"FairXGBoost: Fairness-aware Classification in XGBoost","date":"2020-09-03","arxiv_id":"2009.01442","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-new-training-protocol-for-performance","title":"A Novel Training Protocol for Performance Predictors of Evolutionary Neural Architecture Search Algorithms","date":"2020-08-30","arxiv_id":"2008.13187","n_code_links":0,"syntology":null},{"paper":null,"slug":"introduction-to-logistic-regression","title":"Introduction to logistic regression","date":"2020-08-28","arxiv_id":"2008.13567","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-transfer-learning-of-traditional-frequency","title":"On Transfer Learning of Traditional Frequency and Time Domain Features in Turning","date":"2020-08-28","arxiv_id":"2008.12691","n_code_links":0,"syntology":null},{"paper":null,"slug":"syrapropa-at-semeval-2020-task-11-bert-based","title":"syrapropa at SemEval-2020 Task 11: BERT-based Models Design For Propagandistic Technique and Span Detection","date":"2020-08-24","arxiv_id":"2008.10163","n_code_links":0,"syntology":null},{"paper":"/paper/dnn2lr-interpretation-inspired-feature","slug":"dnn2lr-interpretation-inspired-feature","title":"DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data","date":"2020-08-22","arxiv_id":"2008.09775","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-approaches-to-real-estate","title":"Machine Learning Approaches to Real Estate Market Prediction Problem: A Case Study","date":"2020-08-22","arxiv_id":"2008.09922","n_code_links":0,"syntology":null},{"paper":null,"slug":"when-homomorphic-encryption-marries-secret","title":"When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control","date":"2020-08-20","arxiv_id":"2008.08753","n_code_links":0,"syntology":null},{"paper":null,"slug":"estimation-of-causal-effects-of-multiple","title":"Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes","date":"2020-08-18","arxiv_id":"2008.07687","n_code_links":0,"syntology":null}],"record_sha256":"9b53d1cc514c9ee7b5c15361641416ae0fdfc9b02f1e430332d0d50cfe9fc317","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}