{"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/5","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":5,"pages_in_order":19,"rows_per_page":100,"rows":[401,500],"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/4","next":"/method/logistic-regression/papers/6","papers":[{"paper":"/paper/distributed-markov-chain-monte-carlo-sampling","slug":"distributed-markov-chain-monte-carlo-sampling","title":"Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers","date":"2024-01-29","arxiv_id":"2401.15838","n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-network-training-on-encrypted-data","title":"Neural Network Training on Encrypted Data with TFHE","date":"2024-01-29","arxiv_id":"2401.16136","n_code_links":0,"syntology":null},{"paper":null,"slug":"pericoronary-adipose-tissue-feature-analysis","title":"Pericoronary adipose tissue feature analysis in CT calcium score images with comparison to coronary CTA","date":"2024-01-28","arxiv_id":"2401.15554","n_code_links":0,"syntology":null},{"paper":null,"slug":"prediction-of-breast-cancer-recurrence-risk","title":"Prediction of Breast Cancer Recurrence Risk Using a Multi-Model Approach Integrating Whole Slide Imaging and Clinicopathologic Features","date":"2024-01-28","arxiv_id":"2401.15805","n_code_links":0,"syntology":null},{"paper":"/paper/prevalidated-ridge-regression-is-a-highly","slug":"prevalidated-ridge-regression-is-a-highly","title":"Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data","date":"2024-01-28","arxiv_id":"2401.15610","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancement-of-a-text-independent-speaker","title":"Enhancement of a Text-Independent Speaker Verification System by using Feature Combination and Parallel-Structure Classifiers","date":"2024-01-26","arxiv_id":"2401.15018","n_code_links":0,"syntology":null},{"paper":"/paper/plitterstreet-street-level-plastic-litter","slug":"plitterstreet-street-level-plastic-litter","title":"pLitterStreet: Street Level Plastic Litter Detection and Mapping","date":"2024-01-26","arxiv_id":"2401.14719","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-conversion-rate-prediction-via-self","title":"Improving conversion rate prediction via self-supervised pre-training in online advertising","date":"2024-01-25","arxiv_id":"2401.16432","n_code_links":0,"syntology":null},{"paper":null,"slug":"nonparametric-logistic-regression-with-deep","title":"Nonparametric logistic regression with deep learning","date":"2024-01-23","arxiv_id":"2401.12482","n_code_links":0,"syntology":null},{"paper":null,"slug":"fake-google-restaurant-reviews-and-the","title":"Fake Google restaurant reviews and the implications for consumers and restaurants","date":"2024-01-20","arxiv_id":"2401.11345","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-approach-to-detect-dynamical","title":"Machine learning approach to detect dynamical states from recurrence measures","date":"2024-01-18","arxiv_id":"2401.10298","n_code_links":0,"syntology":null},{"paper":"/paper/towards-predicting-the-quality-of-red-wine","slug":"towards-predicting-the-quality-of-red-wine","title":"Towards Predicting the Quality of Red Wine Using Novel Machine Learning Methods for Classification, Data Visualization and Analysis","date":"2024-01-18","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"a-novel-approach-in-solving-stochastic","title":"A Novel Approach in Solving Stochastic Generalized Linear Regression via Nonconvex Programming","date":"2024-01-16","arxiv_id":"2401.08488","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-based-analysis-of-ebola","title":"Machine Learning-Based Analysis of Ebola Virus' Impact on Gene Expression in Nonhuman Primates","date":"2024-01-16","arxiv_id":"2401.08738","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-based-malicious-vehicle","title":"Machine Learning-Based Malicious Vehicle Detection for Security Threats and Attacks in Vehicle Ad-hoc Network (VANET) Communications","date":"2024-01-16","arxiv_id":"2401.08135","n_code_links":0,"syntology":null},{"paper":null,"slug":"reliability-and-interpretability-in-science","title":"Reliability and Interpretability in Science and Deep Learning","date":"2024-01-14","arxiv_id":"2401.07359","n_code_links":0,"syntology":null},{"paper":"/paper/yolo-former-yolo-shakes-hand-with-vit","slug":"yolo-former-yolo-shakes-hand-with-vit","title":"YOLO-Former: YOLO Shakes Hand With ViT","date":"2024-01-11","arxiv_id":"2401.06244","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-configure-mathematical","title":"Learning to Configure Mathematical Programming Solvers by Mathematical Programming","date":"2024-01-10","arxiv_id":"2401.05041","n_code_links":0,"syntology":null},{"paper":null,"slug":"temporal-analysis-of-world-disaster-risk-a","title":"Temporal Analysis of World Disaster Risk:A Machine Learning Approach to Cluster Dynamics","date":"2024-01-10","arxiv_id":"2401.05007","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-priori-determination-of-the-pretest","title":"A Priori Determination of the Pretest Probability","date":"2024-01-08","arxiv_id":"2401.04086","n_code_links":0,"syntology":null},{"paper":null,"slug":"hypersense-accelerating-hyper-dimensional","title":"HyperSense: Hyperdimensional Intelligent Sensing for Energy-Efficient Sparse Data Processing","date":"2024-01-04","arxiv_id":"2401.10267","n_code_links":0,"syntology":null},{"paper":"/paper/switchtab-switched-autoencoders-are-effective","slug":"switchtab-switched-autoencoders-are-effective","title":"SwitchTab: Switched Autoencoders Are Effective Tabular Learners","date":"2024-01-04","arxiv_id":"2401.02013","n_code_links":1,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"6 ran (of which 6 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; every one of the 6 samples that ran constructed an object rather than computing a result","official":null}},{"paper":null,"slug":"machine-learning-classification-of-alzheimer","title":"Machine Learning Classification of Alzheimer's Disease Stages Using Cerebrospinal Fluid Biomarkers Alone","date":"2024-01-02","arxiv_id":"2401.00981","n_code_links":0,"syntology":null},{"paper":"/paper/self-calibrating-vicinal-risk-minimisation","slug":"self-calibrating-vicinal-risk-minimisation","title":"Self-Calibrating Vicinal Risk Minimisation for Model Calibration","date":"2024-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"kaxai-an-integrated-environment-for-knowledge","title":"KAXAI: An Integrated Environment for Knowledge Analysis and Explainable AI","date":"2023-12-30","arxiv_id":"2401.00193","n_code_links":0,"syntology":null},{"paper":null,"slug":"doepatch-dynamically-optimized-ensemble-model","title":"DOEPatch: Dynamically Optimized Ensemble Model for Adversarial Patches Generation","date":"2023-12-28","arxiv_id":"2312.16907","n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarial-data-poisoning-for-fake-news","title":"Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It","date":"2023-12-23","arxiv_id":"2312.15228","n_code_links":0,"syntology":null},{"paper":null,"slug":"misclassification-excess-risk-bounds-for-1","title":"Misclassification excess risk bounds for 1-bit matrix completion","date":"2023-12-20","arxiv_id":"2312.12945","n_code_links":0,"syntology":null},{"paper":null,"slug":"distributed-bayesian-inference-for-large","title":"Distributed Bayesian Inference for Large-Scale IoT Systems","date":"2023-12-19","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"reasoning-with-random-sets-an-agenda-for-the","title":"Reasoning with random sets: An agenda for the future","date":"2023-12-19","arxiv_id":"2401.09435","n_code_links":0,"syntology":null},{"paper":null,"slug":"determinants-of-hotels-and-restaurants","title":"Determinants of Hotels and Restaurants entrepreneurship: A study using GEM data","date":"2023-12-18","arxiv_id":"2401.13685","n_code_links":0,"syntology":null},{"paper":null,"slug":"knowledge-trees-gradient-boosting-decision","title":"Knowledge Trees: Gradient Boosting Decision Trees on Knowledge Neurons as Probing Classifier","date":"2023-12-17","arxiv_id":"2312.10746","n_code_links":0,"syntology":null},{"paper":null,"slug":"fusion-of-deep-and-shallow-features-for-face","title":"Fusion of Deep and Shallow Features for Face Kinship Verification","date":"2023-12-16","arxiv_id":"2312.10462","n_code_links":0,"syntology":null},{"paper":null,"slug":"prediction-of-crash-injury-severity-in","title":"Prediction of Crash Injury Severity in Florida's Interstate-95","date":"2023-12-16","arxiv_id":"2312.12459","n_code_links":0,"syntology":null},{"paper":null,"slug":"algorithms-for-automatic-intents-extraction","title":"Algorithms for automatic intents extraction and utterances classification for goal-oriented dialogue systems","date":"2023-12-15","arxiv_id":"2312.09658","n_code_links":0,"syntology":null},{"paper":null,"slug":"asset-ownership-identification-using-machine","title":"Asset Ownership Identification: Using machine learning to predict enterprise asset ownership","date":"2023-12-15","arxiv_id":"2312.10266","n_code_links":0,"syntology":null},{"paper":null,"slug":"hypothesis-testing-for-class-conditional","title":"Hypothesis Testing for Class-Conditional Noise Using Local Maximum Likelihood","date":"2023-12-15","arxiv_id":"2312.10238","n_code_links":0,"syntology":null},{"paper":null,"slug":"system-integration-of-xilinx-dpu-and-hdmi-for","title":"System Integration of Xilinx DPU and HDMI for Real-Time inference in PYNQ Environment with Image Enhancement","date":"2023-12-15","arxiv_id":"2312.09506","n_code_links":0,"syntology":null},{"paper":null,"slug":"class-probability-matching-using-kernel","title":"Class Probability Matching Using Kernel Methods for Label Shift Adaptation","date":"2023-12-12","arxiv_id":"2312.07282","n_code_links":0,"syntology":null},{"paper":"/paper/adod-adaptive-domain-aware-object-detection","slug":"adod-adaptive-domain-aware-object-detection","title":"ADOD: Adaptive Domain-Aware Object Detection with Residual Attention for Underwater Environments","date":"2023-12-11","arxiv_id":"2312.06801","n_code_links":1,"syntology":null},{"paper":null,"slug":"classification-with-partially-private","title":"Classification with Partially Private Features","date":"2023-12-11","arxiv_id":"2312.07583","n_code_links":0,"syntology":null},{"paper":null,"slug":"performance-of-externally-validated-machine","title":"Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review","date":"2023-12-09","arxiv_id":"2312.06697","n_code_links":0,"syntology":null},{"paper":null,"slug":"soft-frequency-capping-for-improved-ad-click","title":"Soft Frequency Capping for Improved Ad Click Prediction in Yahoo Gemini Native","date":"2023-12-08","arxiv_id":"2312.05052","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-kinship-verification-through","title":"Enhancing Kinship Verification through Multiscale Retinex and Combined Deep-Shallow features","date":"2023-12-06","arxiv_id":"2312.03562","n_code_links":0,"syntology":null},{"paper":null,"slug":"determinants-of-the-propensity-for-innovation","title":"Determinants of the Propensity for Innovation among Entrepreneurs in the Tourism Industry","date":"2023-12-05","arxiv_id":"2401.13679","n_code_links":0,"syntology":null},{"paper":"/paper/gdn-a-stacking-network-used-for-skin-cancer","slug":"gdn-a-stacking-network-used-for-skin-cancer","title":"GDN: A Stacking Network Used for Skin Cancer Diagnosis","date":"2023-12-05","arxiv_id":"2312.02437","n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-scoring-of-students-science-writing","title":"Automatic Scoring of Students' Science Writing Using Hybrid Neural Network","date":"2023-12-02","arxiv_id":"2312.03752","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-and-analysis-of-stress-related","title":"Detection and Analysis of Stress-Related Posts in Reddit Acamedic Communities","date":"2023-12-02","arxiv_id":"2312.01050","n_code_links":0,"syntology":null},{"paper":"/paper/toward-improving-robustness-of-object","slug":"toward-improving-robustness-of-object","title":"Toward Improving Robustness of Object Detectors Against Domain Shift","date":"2023-12-02","arxiv_id":"2403.12049","n_code_links":1,"syntology":null},{"paper":"/paper/object-detector-differences-when-using","slug":"object-detector-differences-when-using","title":"Object Detector Differences when using Synthetic and Real Training Data","date":"2023-12-01","arxiv_id":"2312.00694","n_code_links":1,"syntology":null},{"paper":null,"slug":"protoargnet-interpretable-image","title":"ProtoArgNet: Interpretable Image Classification with Super-Prototypes and Argumentation [Technical Report]","date":"2023-11-26","arxiv_id":"2311.15438","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-sentiment-analysis-results-through","title":"Enhancing Sentiment Analysis Results through Outlier Detection Optimization","date":"2023-11-25","arxiv_id":"2311.16185","n_code_links":0,"syntology":null},{"paper":null,"slug":"disruption-prediction-in-fusion-devices","title":"Disruption Prediction in Fusion Devices through Feature Extraction and Logistic Regression","date":"2023-11-24","arxiv_id":"2311.14856","n_code_links":0,"syntology":null},{"paper":null,"slug":"touch-analysis-an-empirical-evaluation-of","title":"Touch Analysis: An Empirical Evaluation of Machine Learning Classification Algorithms on Touch Data","date":"2023-11-23","arxiv_id":"2311.14195","n_code_links":0,"syntology":null},{"paper":"/paper/fast-and-interpretable-mortality-risk-scores","slug":"fast-and-interpretable-mortality-risk-scores","title":"Fast and Interpretable Mortality Risk Scores for Critical Care Patients","date":"2023-11-21","arxiv_id":"2311.13015","n_code_links":1,"syntology":null},{"paper":null,"slug":"quantum-enhanced-support-vector-machine-for","title":"Quantum-Enhanced Support Vector Machine for Large-Scale Stellar Classification with GPU Acceleration","date":"2023-11-21","arxiv_id":"2311.12328","n_code_links":0,"syntology":null},{"paper":"/paper/dreifluss-a-minimalist-approach-for-table","slug":"dreifluss-a-minimalist-approach-for-table","title":"DREIFLUSS: A Minimalist Approach for Table Matching","date":"2023-11-20","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"incorporating-llm-priors-into-tabular","title":"Incorporating LLM Priors into Tabular Learners","date":"2023-11-20","arxiv_id":"2311.11628","n_code_links":0,"syntology":null},{"paper":null,"slug":"multiscale-hodge-scattering-networks-for-data","title":"Multiscale Hodge Scattering Networks for Data Analysis","date":"2023-11-17","arxiv_id":"2311.10270","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantum-data-encoding-a-comparative-analysis","title":"Quantum Data Encoding: A Comparative Analysis of Classical-to-Quantum Mapping Techniques and Their Impact on Machine Learning Accuracy","date":"2023-11-17","arxiv_id":"2311.10375","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-unified-approach-to-learning-ising-models","title":"A Unified Approach to Learning Ising Models: Beyond Independence and Bounded Width","date":"2023-11-15","arxiv_id":"2311.09197","n_code_links":0,"syntology":null},{"paper":"/paper/comparison-of-artificial-intelligence-models","slug":"comparison-of-artificial-intelligence-models","title":"Comparison of artificial intelligence models for prognosis of breast cancer","date":"2023-11-15","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/expm-nf-differentially-private-machine","slug":"expm-nf-differentially-private-machine","title":"Are Normalizing Flows the Key to Unlocking the Exponential Mechanism?","date":"2023-11-15","arxiv_id":"2311.09200","n_code_links":1,"syntology":null},{"paper":null,"slug":"strategic-data-augmentation-with-ctgan-for","title":"Strategic Data Augmentation with CTGAN for Smart Manufacturing: Enhancing Machine Learning Predictions of Paper Breaks in Pulp-and-Paper Production","date":"2023-11-15","arxiv_id":"2311.09333","n_code_links":0,"syntology":null},{"paper":null,"slug":"clinical-characteristics-and-laboratory","title":"Clinical Characteristics and Laboratory Biomarkers in ICU-admitted Septic Patients with and without Bacteremia","date":"2023-11-14","arxiv_id":"2311.08433","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-semi-supervised-estimation-using","title":"On semi-supervised estimation using exponential tilt mixture models","date":"2023-11-14","arxiv_id":"2311.08504","n_code_links":0,"syntology":null},{"paper":null,"slug":"yolov5s-bc-an-improved-yolov5s-based-method","title":"YOLOv5s-BC: An improved YOLOv5s-based method for real-time apple detection","date":"2023-11-10","arxiv_id":"2311.05811","n_code_links":0,"syntology":null},{"paper":"/paper/determination-of-toxic-comments-and","slug":"determination-of-toxic-comments-and","title":"Determination of toxic comments and unintended model bias minimization using Deep learning approach","date":"2023-11-08","arxiv_id":"2311.04789","n_code_links":1,"syntology":null},{"paper":"/paper/dimensions-of-online-conflict-towards","slug":"dimensions-of-online-conflict-towards","title":"Dimensions of Online Conflict: Towards Modeling Agonism","date":"2023-11-06","arxiv_id":"2311.03584","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-cardiovascular-disease-prediction","title":"Improving Cardiovascular Disease Prediction Through Comparative Analysis of Machine Learning Models: A Case Study on Myocardial Infarction","date":"2023-11-01","arxiv_id":"2311.00517","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-recovery-following-stroke-deep","title":"Predicting recovery following stroke: deep learning, multimodal data and feature selection using explainable AI","date":"2023-10-29","arxiv_id":"2310.19174","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-kernel-surrogates-for-neural","title":"Efficient kernel surrogates for neural network-based regression","date":"2023-10-28","arxiv_id":"2310.18612","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-epileptic-seizure-detection-with","title":"Enhancing Epileptic Seizure Detection with EEG Feature Embeddings","date":"2023-10-28","arxiv_id":"2310.18767","n_code_links":0,"syntology":null},{"paper":null,"slug":"cosmosdsr-a-methodology-for-automated","title":"CosmosDSR -- a methodology for automated detection and tracking of orbital debris using the Unscented Kalman Filter","date":"2023-10-26","arxiv_id":"2310.17158","n_code_links":0,"syntology":null},{"paper":null,"slug":"inside-the-black-box-neural-network-based","title":"Inside the black box: Neural network-based real-time prediction of US recessions","date":"2023-10-26","arxiv_id":"2310.17571","n_code_links":0,"syntology":null},{"paper":null,"slug":"locally-differentially-private-gradient","title":"Locally Differentially Private Gradient Tracking for Distributed Online Learning over Directed Graphs","date":"2023-10-24","arxiv_id":"2310.16105","n_code_links":0,"syntology":null},{"paper":null,"slug":"guidance-system-for-visually-impaired-persons","title":"Guidance system for Visually Impaired Persons using Deep Learning and Optical flow","date":"2023-10-22","arxiv_id":"2310.14239","n_code_links":0,"syntology":null},{"paper":null,"slug":"application-of-deep-learning-for-livestock","title":"Application of deep learning for livestock behaviour recognition: A systematic literature review","date":"2023-10-20","arxiv_id":"2310.13483","n_code_links":0,"syntology":null},{"paper":null,"slug":"sigml-supervised-log-anomaly-with","title":"SigML++: Supervised Log Anomaly with Probabilistic Polynomial Approximation","date":"2023-10-19","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/compositional-preference-models-for-aligning","slug":"compositional-preference-models-for-aligning","title":"Compositional preference models for aligning LMs","date":"2023-10-17","arxiv_id":"2310.13011","n_code_links":1,"syntology":{"ran":11,"of":13,"n_ran_checked":11,"n_instrument":0,"unverified":2,"pointer_only":13,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["dongyoung-go/cpm"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"gender-based-comparative-study-of-type-2","title":"Gender-Based Comparative Study of Type 2 Diabetes Risk Factors in Kolkata, India: A Machine Learning Approach","date":"2023-10-15","arxiv_id":"2311.10731","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-generative-ai-improving-software","title":"Leveraging Generative AI: Improving Software Metadata Classification with Generated Code-Comment Pairs","date":"2023-10-14","arxiv_id":"2311.03365","n_code_links":0,"syntology":null},{"paper":null,"slug":"insuring-smiles-predicting-routine-dental","title":"Insuring Smiles: Predicting routine dental coverage using Spark ML","date":"2023-10-13","arxiv_id":"2310.09229","n_code_links":0,"syntology":null},{"paper":null,"slug":"divorce-prediction-with-machine-learning","title":"Divorce Prediction with Machine Learning: Insights and LIME Interpretability","date":"2023-10-12","arxiv_id":"2310.08620","n_code_links":0,"syntology":null},{"paper":null,"slug":"real-time-prediction-of-the-great-recession","title":"Real-time Prediction of the Great Recession and the Covid-19 Recession","date":"2023-10-12","arxiv_id":"2310.08536","n_code_links":0,"syntology":null},{"paper":"/paper/when-why-and-how-much-adaptive-learning-rate","slug":"when-why-and-how-much-adaptive-learning-rate","title":"Optimal Linear Decay Learning Rate Schedules and Further Refinements","date":"2023-10-11","arxiv_id":"2310.07831","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"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","official":{"repos":["facebookresearch/adaptive_scheduling"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"cost-sensitive-best-subset-selection-for","title":"Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective","date":"2023-10-09","arxiv_id":"2310.05464","n_code_links":0,"syntology":null},{"paper":"/paper/how-to-effectively-train-an-ensemble-of","slug":"how-to-effectively-train-an-ensemble-of","title":"How To Effectively Train An Ensemble Of Faster R-CNN Object Detectors To Quantify Uncertainty","date":"2023-10-07","arxiv_id":"2310.04829","n_code_links":1,"syntology":null},{"paper":null,"slug":"robust-transfer-learning-with-unreliable","title":"Robust Transfer Learning with Unreliable Source Data","date":"2023-10-06","arxiv_id":"2310.04606","n_code_links":0,"syntology":null},{"paper":null,"slug":"parking-spot-classification-based-on-surround","title":"Parking Spot Classification based on surround view camera system","date":"2023-10-05","arxiv_id":"2310.12997","n_code_links":0,"syntology":null},{"paper":null,"slug":"computationally-efficient-quadratic-neural","title":"Efficient Vectorized Backpropagation Algorithms for Training Feedforward Networks Composed of Quadratic Neurons","date":"2023-10-04","arxiv_id":"2310.02901","n_code_links":0,"syntology":null},{"paper":null,"slug":"5g-network-slicing-analysis-of-multiple","title":"5G Network Slicing: Analysis of Multiple Machine Learning Classifiers","date":"2023-10-03","arxiv_id":"2310.01747","n_code_links":0,"syntology":null},{"paper":"/paper/beyond-the-benchmark-detecting-diverse","slug":"beyond-the-benchmark-detecting-diverse","title":"Beyond the Benchmark: Detecting Diverse Anomalies in Videos","date":"2023-10-03","arxiv_id":"2310.01904","n_code_links":1,"syntology":null},{"paper":"/paper/short-text-classification-with-machine","slug":"short-text-classification-with-machine","title":"Short text classification with machine learning in the social sciences: The case of climate change on Twitter","date":"2023-10-03","arxiv_id":"2310.04452","n_code_links":1,"syntology":null},{"paper":"/paper/light-schrodinger-bridge","slug":"light-schrodinger-bridge","title":"Light Schrödinger Bridge","date":"2023-10-02","arxiv_id":"2310.01174","n_code_links":1,"syntology":{"ran":9,"of":13,"n_ran_checked":7,"n_instrument":2,"unverified":4,"pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","official":{"repos":["ngushchin/lightsb"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"age-group-discrimination-via-free-handwriting","title":"Age Group Discrimination via Free Handwriting Indicators","date":"2023-09-29","arxiv_id":"2309.17156","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-based-analytics-for-the","title":"Machine Learning Based Analytics for the Significance of Gait Analysis in Monitoring and Managing Lower Extremity Injuries","date":"2023-09-27","arxiv_id":"2309.15990","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-log-concavity-theory-and-algorithm-for","title":"Beyond Log-Concavity: Theory and Algorithm for Sum-Log-Concave Optimization","date":"2023-09-26","arxiv_id":"2309.15298","n_code_links":0,"syntology":null},{"paper":"/paper/how-to-catch-an-ai-liar-lie-detection-in","slug":"how-to-catch-an-ai-liar-lie-detection-in","title":"How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions","date":"2023-09-26","arxiv_id":"2309.15840","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":0,"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) · 1 unverified","official":{"repos":["lorypack/llm-liedetector"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"predicting-environment-effects-on-breast","title":"Predicting environment effects on breast cancer by implementing machine learning","date":"2023-09-25","arxiv_id":"2309.14397","n_code_links":0,"syntology":null}],"record_sha256":"fffe16426c92e42d7a919aea2da779b1399133ae26039dd2b510f242b4d5852d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}