{"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/machine-learning/papers/70","list_of":"/task/machine-learning","task":"BIG-bench Machine Learning","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":70,"pages_in_order":101,"rows_per_page":100,"rows":[6901,7000],"of":10033,"counts":{"archive_papers_tagged":10033,"with_a_code_link":2352,"where_syntology_ran_a_sample":356,"not_listed_spam_title":0,"listed":10033,"listed_where_code_ran":356,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":290,"every_run_a_failure_of_syntologys_instrument":66,"listed_with_a_run_with_no_instrument_failure":290,"listed_every_run_a_failure_of_syntologys_instrument":66,"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/machine-learning","prev":"/task/machine-learning/papers/69","next":"/task/machine-learning/papers/71","papers":[{"url":null,"slug":"integrating-machine-learning-and-multiscale","title":"Integrating Machine Learning and Multiscale Modeling: Perspectives, Challenges, and Opportunities in the Biological, Biomedical, and Behavioral Sciences","date":"2019-10-24","arxiv_id":"1910.01258","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-scent-learning","title":"Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules","date":"2019-10-23","arxiv_id":"1910.10685","repositories_listed":0,"syntology":null},{"url":null,"slug":"pharmlbind-pharmacologic-machine-learning-for","title":"PharML.Bind: Pharmacologic Machine Learning for Protein-Ligand Interactions","date":"2019-10-23","arxiv_id":"1911.06105","repositories_listed":0,"syntology":null},{"url":null,"slug":"q-gadmm-quantized-group-admm-for","title":"Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning","date":"2019-10-23","arxiv_id":"1910.10453","repositories_listed":0,"syntology":null},{"url":null,"slug":"suicidal-ideation-detection-a-review-of","title":"Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications","date":"2019-10-23","arxiv_id":"1910.12611","repositories_listed":0,"syntology":null},{"url":null,"slug":"trojan-attacks-on-wireless-signal","title":"Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning","date":"2019-10-23","arxiv_id":"1910.10766","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-intelligence-and-the-future-of-1","title":"Artificial Intelligence and the Future of Psychiatry: Qualitative Findings from a Global Physician Survey","date":"2019-10-22","arxiv_id":"1910.09956","repositories_listed":0,"syntology":null},{"url":null,"slug":"autonomous-discovery-of-battery-electrolytes","title":"Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning","date":"2019-10-22","arxiv_id":"2001.09938","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-and-serving-of-discrete","title":"Machine learning and serving of discrete field theories -- when artificial intelligence meets the discrete universe","date":"2019-10-22","arxiv_id":"1910.10147","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-single-mosfet-mac-for-confidence-and","title":"A Single-MOSFET MAC for Confidence and Resolution (CORE) Driven Machine Learning Classification","date":"2019-10-21","arxiv_id":"1910.09597","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-learned-bloom-filter-ada-bf","title":"Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier","date":"2019-10-21","arxiv_id":"1910.09131","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-optimization-allowing-for-common","title":"Bayesian Optimization Allowing for Common Random Numbers","date":"2019-10-21","arxiv_id":"1910.09259","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-bootstrapping","title":"Causal bootstrapping","date":"2019-10-21","arxiv_id":"1910.09648","repositories_listed":0,"syntology":null},{"url":null,"slug":"coercing-machine-learning-to-output","title":"Coercing Machine Learning to Output Physically Accurate Results","date":"2019-10-21","arxiv_id":"1910.09671","repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-local-switching-fields-in","title":"Extracting local switching fields in permanent magnets using machine learning","date":"2019-10-21","arxiv_id":"1910.09279","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-order-selection-in-doa-scenarios-via","title":"Model Order Selection in DoA Scenarios via Cross-Entropy based Machine Learning Techniques","date":"2019-10-21","arxiv_id":"1910.09284","repositories_listed":0,"syntology":null},{"url":null,"slug":"shallow-art-art-extension-through-simple","title":"Shallow Art: Art Extension Through Simple Machine Learning","date":"2019-10-21","arxiv_id":"1910.11118","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-automatic-comparison-of-visualization","title":"Toward automatic comparison of visualization techniques: Application to graph visualization","date":"2019-10-21","arxiv_id":"1910.09477","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-case-studies-of-experience-prototyping","title":"Two Case Studies of Experience Prototyping Machine Learning Systems in the Wild","date":"2019-10-21","arxiv_id":"1910.09137","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-machine-learning-and-information","title":"Using machine learning and information visualisation for discovering latent topics in Twitter news","date":"2019-10-21","arxiv_id":"1910.09114","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-hierarchical-representations-for","title":"Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text","date":"2019-10-20","arxiv_id":"1910.08917","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-ice-flow-using-machine-learning","title":"Predicting ice flow using machine learning","date":"2019-10-20","arxiv_id":"1910.08922","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-for-space-time","title":"Convolutional Neural Networks for Space-Time Block Coding Recognition","date":"2019-10-19","arxiv_id":"1910.09952","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-ac-optimal-power-flow","title":"Machine Learning for AC Optimal Power Flow","date":"2019-10-19","arxiv_id":"1910.08842","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-based-framework-for-the","title":"A Deep Learning-based Framework for the Detection of Schools of Herring in Echograms","date":"2019-10-18","arxiv_id":"1910.08215","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-topological-reading-lesson-classification","title":"A Topological \"Reading\" Lesson: Classification of MNIST using TDA","date":"2019-10-18","arxiv_id":"1910.08345","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-partitioning-for-template-functions","title":"Adaptive Partitioning for Template Functions on Persistence Diagrams","date":"2019-10-18","arxiv_id":"1910.08506","repositories_listed":0,"syntology":null},{"url":null,"slug":"error-lower-bounds-of-constant-step-size","title":"Error Lower Bounds of Constant Step-size Stochastic Gradient Descent","date":"2019-10-18","arxiv_id":"1910.08212","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecasting-the-success-of-television-series","title":"Forecasting the Success of Television Series using Machine Learning","date":"2019-10-18","arxiv_id":"1910.12589","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-the-most-explainable-classifier","title":"Identifying the Most Explainable Classifier","date":"2019-10-18","arxiv_id":"1910.08595","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-calabi-yau-metrics","title":"Machine learning Calabi-Yau metrics","date":"2019-10-18","arxiv_id":"1910.08605","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-systems-for-highly","title":"Machine Learning Systems for Highly-Distributed and Rapidly-Growing Data","date":"2019-10-18","arxiv_id":"1910.08663","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-treatment-for-coronary-artery","title":"Personalized Treatment for Coronary Artery Disease Patients: A Machine Learning Approach","date":"2019-10-18","arxiv_id":"1910.08483","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-machine-learning-based-ensemble","title":"Supervised Machine Learning based Ensemble Model for Accurate Prediction of Type 2 Diabetes","date":"2019-10-18","arxiv_id":"1910.09356","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-asynchronous","title":"Communication-Efficient Asynchronous Stochastic Frank-Wolfe over Nuclear-norm Balls","date":"2019-10-17","arxiv_id":"1910.07703","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-on-sweighted-data","title":"Machine Learning on sWeighted Data","date":"2019-10-17","arxiv_id":"1912.02590","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-text-summarization-for-the","title":"Automated Text Summarization for the Enhancement of Public Services","date":"2019-10-16","arxiv_id":"1910.10490","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-tracking-in-dynamic-probabilistic","title":"Graph Tracking in Dynamic Probabilistic Programs via Source Transformations","date":"2019-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-based-identification-of","title":"Machine learning based identification of buried objects using sparse whitened NMF","date":"2019-10-16","arxiv_id":"1910.07180","repositories_listed":0,"syntology":null},{"url":null,"slug":"transform-the-set-memory-attentive-generation","title":"Transform the Set: Memory Attentive Generation of Guided and Unguided Image Collages","date":"2019-10-16","arxiv_id":"1910.07236","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-intelligent-data-analysis-for-hotel","title":"An Intelligent Data Analysis for Hotel Recommendation Systems using Machine Learning","date":"2019-10-15","arxiv_id":"1910.06669","repositories_listed":0,"syntology":null},{"url":null,"slug":"cascading-machine-learning-to-attack-bitcoin","title":"Cascading Machine Learning to Attack Bitcoin Anonymity","date":"2019-10-15","arxiv_id":"1910.06560","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-matters-recovering-human-semantic","title":"Context Matters: Recovering Human Semantic Structure from Machine Learning Analysis of Large-Scale Text Corpora","date":"2019-10-15","arxiv_id":"1910.06954","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-generalizable-prediction","title":"Machine Learning for Generalizable Prediction of Flood Susceptibility","date":"2019-10-15","arxiv_id":"1910.06521","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-the-completeness-of-theories","title":"Measuring the Completeness of Theories","date":"2019-10-15","arxiv_id":"1910.07022","repositories_listed":0,"syntology":null},{"url":null,"slug":"shapley-homology-topological-analysis-of","title":"Shapley Homology: Topological Analysis of Sample Influence for Neural Networks","date":"2019-10-15","arxiv_id":"1910.06509","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-for-algorithm","title":"Transfer Learning for Algorithm Recommendation","date":"2019-10-15","arxiv_id":"1910.07012","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-damage-detection-in-satellite","title":"Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks","date":"2019-10-14","arxiv_id":"1910.06444","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-information-modeling-and","title":"Building Information Modeling and Classification by Visual Learning At A City Scale","date":"2019-10-14","arxiv_id":"1910.06391","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-for-coalition-operations","title":"Federated Learning for Coalition Operations","date":"2019-10-14","arxiv_id":"1910.06799","repositories_listed":0,"syntology":null},{"url":null,"slug":"firenet-real-time-segmentation-of-fire","title":"FireNet: Real-time Segmentation of Fire Perimeter from Aerial Video","date":"2019-10-14","arxiv_id":"1910.06407","repositories_listed":0,"syntology":null},{"url":null,"slug":"man-in-the-middle-attacks-against-machine","title":"Man-in-the-Middle Attacks against Machine Learning Classifiers via Malicious Generative Models","date":"2019-10-14","arxiv_id":"1910.06838","repositories_listed":0,"syntology":null},{"url":null,"slug":"sampling-based-sublinear-low-rank-matrix","title":"Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning","date":"2019-10-14","arxiv_id":"1910.06151","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-prediction-with-missing","title":"Machine Learning for Prediction with Missing Dynamics","date":"2019-10-13","arxiv_id":"1910.05861","repositories_listed":0,"syntology":null},{"url":null,"slug":"geomancer-an-open-source-framework-for","title":"Geomancer: An Open-Source Framework for Geospatial Feature Engineering","date":"2019-10-12","arxiv_id":"1910.05571","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-epigenetic-signature-of-breast","title":"Identifying Epigenetic Signature of Breast Cancer with Machine Learning","date":"2019-10-12","arxiv_id":"1910.06899","repositories_listed":0,"syntology":null},{"url":null,"slug":"isolation-and-localization-of-unknown-faults","title":"Isolation and Localization of Unknown Faults Using Neural Network-Based Residuals","date":"2019-10-12","arxiv_id":"1910.05626","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-and-big-scientific-data","title":"Machine Learning and Big Scientific Data","date":"2019-10-12","arxiv_id":"1910.07631","repositories_listed":0,"syntology":null},{"url":null,"slug":"preliminary-systematic-literature-review-of","title":"Preliminary Systematic Literature Review of Machine Learning System Development Process","date":"2019-10-12","arxiv_id":"1910.05528","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-general-scoring-rule-for-randomized-kernel","title":"ORCCA: Optimal Randomized Canonical Correlation Analysis","date":"2019-10-11","arxiv_id":"1910.05384","repositories_listed":0,"syntology":null},{"url":null,"slug":"geovisual-analytics-and-interactive-machine","title":"Geovisual Analytics and Interactive Machine Learning for Situational Awareness","date":"2019-10-11","arxiv_id":"1910.05441","repositories_listed":0,"syntology":null},{"url":null,"slug":"orchestrating-development-lifecycle-of","title":"Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey","date":"2019-10-11","arxiv_id":"1910.05433","repositories_listed":0,"syntology":null},{"url":null,"slug":"relation-learning-in-a-neurocomputational","title":"A Theory of Relation Learning and Cross-domain Generalization","date":"2019-10-11","arxiv_id":"1910.05065","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-graph-wavelet-transform-as-feature","title":"Spectral Graph Wavelet Transform as Feature Extractor for Machine Learning in Neuroimaging","date":"2019-10-11","arxiv_id":"1910.05149","repositories_listed":0,"syntology":null},{"url":null,"slug":"dialog-on-a-canvas-with-a-machine","title":"Dialog on a canvas with a machine","date":"2019-10-10","arxiv_id":"1910.04386","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-neural-network-architecture-for","title":"Dual Neural Network Architecture for Determining Epistemic and Aleatoric Uncertainties","date":"2019-10-10","arxiv_id":"1910.06153","repositories_listed":0,"syntology":null},{"url":null,"slug":"hindsight-analysis-of-the-chicago-food","title":"Hindsight Analysis of the Chicago Food Inspection Forecasting Model","date":"2019-10-10","arxiv_id":"1910.04906","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-driven-synthesis-of-few","title":"Machine learning driven synthesis of few-layered WTe2","date":"2019-10-10","arxiv_id":"1910.04603","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-with-multi-site-imaging-data","title":"Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects","date":"2019-10-10","arxiv_id":"1910.04597","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-free-prediction-of-spatiotemporal","title":"Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius","date":"2019-10-10","arxiv_id":"1910.04426","repositories_listed":0,"syntology":null},{"url":null,"slug":"studying-software-engineering-patterns-for","title":"Studying Software Engineering Patterns for Designing Machine Learning Systems","date":"2019-10-10","arxiv_id":"1910.04736","repositories_listed":0,"syntology":null},{"url":null,"slug":"provenance-data-in-the-machine-learning","title":"Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering","date":"2019-10-09","arxiv_id":"1910.04223","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-machine-learning-model-for-long-term-power","title":"A Machine Learning Model for Long-Term Power Generation Forecasting at Bidding Zone Level","date":"2019-10-08","arxiv_id":"1910.03276","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-federated-learning-via-momentum","title":"Accelerating Federated Learning via Momentum Gradient Descent","date":"2019-10-08","arxiv_id":"1910.03197","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-an-automated-machine-learning","title":"Analysis of an Automated Machine Learning Approach in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures","date":"2019-10-08","arxiv_id":"1910.03349","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-we-distinguish-machine-learning-from","title":"Can We Distinguish Machine Learning from Human Learning?","date":"2019-10-08","arxiv_id":"1910.03466","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-based-biomedical-data-science-2019","title":"Knowledge-based Biomedical Data Science 2019","date":"2019-10-08","arxiv_id":"1910.06710","repositories_listed":0,"syntology":null},{"url":null,"slug":"random-forest-model-identifies-serve-strength","title":"Random forest model identifies serve strength as a key predictor of tennis match outcome","date":"2019-10-08","arxiv_id":"1910.03203","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-paris-end-of-town-urban-typology-through","title":"The 'Paris-end' of town? Urban typology through machine learning","date":"2019-10-08","arxiv_id":"1910.03220","repositories_listed":0,"syntology":null},{"url":null,"slug":"traffickcam-explainable-image-matching-for","title":"TraffickCam: Explainable Image Matching For Sex Trafficking Investigations","date":"2019-10-08","arxiv_id":"1910.03455","repositories_listed":0,"syntology":null},{"url":null,"slug":"algorithmic-probability-guided-supervised","title":"Algorithmic Probability-guided Supervised Machine Learning on Non-differentiable Spaces","date":"2019-10-07","arxiv_id":"1910.02758","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-machine-learning-in","title":"Application of Machine Learning in Forecasting International Trade Trends","date":"2019-10-07","arxiv_id":"1910.03112","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalization-of-machine-learned-turbulent","title":"Generalization of machine-learned turbulent heat flux models applied to film cooling flows","date":"2019-10-07","arxiv_id":"1910.03097","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-machine-learning-for-flood","title":"Multi-Modal Machine Learning for Flood Detection in News, Social Media and Satellite Sequences","date":"2019-10-07","arxiv_id":"1910.02932","repositories_listed":0,"syntology":null},{"url":null,"slug":"organization-of-ml-based-product-development","title":"Organization of machine learning based product development as per ISO 26262 and ISO/PAS 21448","date":"2019-10-07","arxiv_id":"1910.05112","repositories_listed":0,"syntology":null},{"url":null,"slug":"early-prediction-of-30-day-icu-re-admissions","title":"Early Prediction of 30-day ICU Re-admissions Using Natural Language Processing and Machine Learning","date":"2019-10-06","arxiv_id":"1910.02545","repositories_listed":0,"syntology":null},{"url":null,"slug":"migration-through-machine-learning-lens","title":"Migration through Machine Learning Lens -- Predicting Sexual and Reproductive Health Vulnerability of Young Migrants","date":"2019-10-06","arxiv_id":"1910.02390","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-deep-learning-and-machine-learning-to","title":"Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data","date":"2019-10-06","arxiv_id":"1910.02544","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-machine-learning-analysis-of-the-features","title":"A Machine Learning Analysis of the Features in Deceptive and Credible News","date":"2019-10-05","arxiv_id":"1910.02223","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiplierless-and-sparse-machine-learning","title":"Multiplierless and Sparse Machine Learning based on Margin Propagation Networks","date":"2019-10-05","arxiv_id":"1910.02304","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-data-preparation-on-the","title":"The Impact of Data Preparation on the Fairness of Software Systems","date":"2019-10-05","arxiv_id":"1910.02321","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-rademacher-complexity-based-method-fo","title":"A Rademacher Complexity Based Method fo rControlling Power and Confidence Level in Adaptive Statistical Analysis","date":"2019-10-04","arxiv_id":"1910.03493","repositories_listed":0,"syntology":null},{"url":null,"slug":"confederated-machine-learning-on-horizontally","title":"Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence","date":"2019-10-04","arxiv_id":"1910.02109","repositories_listed":0,"syntology":null},{"url":null,"slug":"introduction-to-concentration-inequalities","title":"Introduction to Concentration Inequalities","date":"2019-10-04","arxiv_id":"1910.02884","repositories_listed":0,"syntology":null},{"url":null,"slug":"pinfer-privacy-preserving-inference-for","title":"PINFER: Privacy-Preserving Inference for Machine Learning","date":"2019-10-04","arxiv_id":"1910.01865","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-machine-learning-method-correlating-pulse","title":"A machine learning method correlating pulse pressure wave data with pregnancy","date":"2019-10-03","arxiv_id":"1910.01726","repositories_listed":0,"syntology":null},{"url":null,"slug":"escaping-saddle-points-for-zeroth-order","title":"Escaping Saddle Points for Zeroth-order Nonconvex Optimization using Estimated Gradient Descent","date":"2019-10-03","arxiv_id":"1910.01277","repositories_listed":0,"syntology":null},{"url":null,"slug":"labelsens-enabling-real-time-sensor-data","title":"LabelSens: Enabling Real-time Sensor Data Labelling at the point of Collection on Edge Computing","date":"2019-10-03","arxiv_id":"1910.01400","repositories_listed":0,"syntology":null},{"url":null,"slug":"partial-differential-equation-regularization","title":"Partial differential equation regularization for supervised machine learning","date":"2019-10-03","arxiv_id":"1910.01612","repositories_listed":0,"syntology":null},{"url":null,"slug":"path-planning-microswimmers-can-swim","title":"Machine learning strategies for path-planning microswimmers in turbulent flows","date":"2019-10-03","arxiv_id":"1910.01728","repositories_listed":0,"syntology":null}],"record_sha256":"d5f8d1bec692075cfae455c950c88a08c44febaa7a756bbd2ffd5a6ea9304b7c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}