{"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/66","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":66,"pages_in_order":101,"rows_per_page":100,"rows":[6501,6600],"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/65","next":"/task/machine-learning/papers/67","papers":[{"url":null,"slug":"physics-guided-machine-learning-for","title":"Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles","date":"2020-01-28","arxiv_id":"2001.11086","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-gaussian-process","title":"Privacy-Preserving Gaussian Process Regression -- A Modular Approach to the Application of Homomorphic Encryption","date":"2020-01-28","arxiv_id":"2001.10893","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-out-of-distribution-detection-in","title":"Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems","date":"2020-01-28","arxiv_id":"2001.10494","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-robust-real-time-computing-based","title":"A Robust Real-Time Computing-based Environment Sensing System for Intelligent Vehicle","date":"2020-01-27","arxiv_id":"2001.09678","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-prediction-model-of-components","title":"Data-Driven Prediction Model of Components Shift during Reflow Process in Surface Mount Technology","date":"2020-01-27","arxiv_id":"2001.09619","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimation-of-high-frequency-nutrient","title":"Estimation of high frequency nutrient concentrations from water quality surrogates using machine learning methods","date":"2020-01-27","arxiv_id":"2001.09695","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-in-machine-learning-renyi","title":"Feature selection in machine learning: Rényi min-entropy vs Shannon entropy","date":"2020-01-27","arxiv_id":"2001.09654","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-a-music-glove-instrument","title":"Machine Learning for a Music Glove Instrument","date":"2020-01-27","arxiv_id":"2001.09551","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-explanation-does-not-fit-all-the-promise","title":"One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency","date":"2020-01-27","arxiv_id":"2001.09734","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-analysis-and-comparison-of","title":"Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification","date":"2020-01-27","arxiv_id":"2001.09636","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-final-frontier-deep-learning-in-space","title":"The Final Frontier: Deep Learning in Space","date":"2020-01-27","arxiv_id":"2001.10362","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-artificial-intelligence-and","title":"Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective","date":"2020-01-26","arxiv_id":"2001.09464","repositories_listed":0,"syntology":null},{"url":null,"slug":"applying-machine-learning-algorithms-for","title":"Applying Machine Learning Algorithms for Kidney Disease Diagnosis","date":"2020-01-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-aided-design-of-thinned","title":"Machine Learning-aided Design of Thinned Antenna Arrays for Optimized Network Level Performance","date":"2020-01-25","arxiv_id":"2001.09335","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-based-machine-learning-for-joint","title":"Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation","date":"2020-01-25","arxiv_id":"2001.09277","repositories_listed":0,"syntology":null},{"url":null,"slug":"radiomics-and-artificial-intelligence","title":"Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma","date":"2020-01-24","arxiv_id":"2001.08924","repositories_listed":0,"syntology":null},{"url":null,"slug":"reasoning-about-generalization-via","title":"Reasoning About Generalization via Conditional Mutual Information","date":"2020-01-24","arxiv_id":"2001.09122","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-wireless-security-meets-machine-learning","title":"When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions","date":"2020-01-24","arxiv_id":"2001.08883","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-tumor-classification-using-deep","title":"Brain Tumor Classification Using Deep Learning Technique -- A Comparison between Cropped, Uncropped, and Segmented Lesion Images with Different Sizes","date":"2020-01-23","arxiv_id":"2001.08844","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-machine-learning-control-robust","title":"Explainable Machine Learning Control -- robust control and stability analysis","date":"2020-01-23","arxiv_id":"2001.10056","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-generalisation-of-automl-systems","title":"Improving generalisation of AutoML systems with dynamic fitness evaluations","date":"2020-01-23","arxiv_id":"2001.08842","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-based-co-creative-design","title":"Machine learning based co-creative design framework","date":"2020-01-23","arxiv_id":"2001.08791","repositories_listed":0,"syntology":null},{"url":null,"slug":"inference-over-wireless-iot-links-with","title":"Inference over Wireless IoT Links with Importance-Filtered Updates","date":"2020-01-22","arxiv_id":"2001.07857","repositories_listed":0,"syntology":null},{"url":null,"slug":"intermittent-pulling-with-local-compensation","title":"Intermittent Pulling with Local Compensation for Communication-Efficient Federated Learning","date":"2020-01-22","arxiv_id":"2001.08277","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-assisted-handover-and","title":"Machine Learning assisted Handover and Resource Management for Cellular Connected Drones","date":"2020-01-22","arxiv_id":"2001.07937","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-network-slicing-resource","title":"Machine Learning for Network Slicing Resource Management: A Comprehensive Survey","date":"2020-01-22","arxiv_id":"2001.07974","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimized-generic-feature-learning-for-few","title":"Optimized Generic Feature Learning for Few-shot Classification across Domains","date":"2020-01-22","arxiv_id":"2001.07926","repositories_listed":0,"syntology":null},{"url":null,"slug":"zeroth-order-algorithms-for-nonconvex-minimax","title":"Zeroth-Order Algorithms for Nonconvex Minimax Problems with Improved Complexities","date":"2020-01-22","arxiv_id":"2001.07819","repositories_listed":0,"syntology":null},{"url":null,"slug":"designing-for-the-long-tail-of-machine","title":"Designing for the Long Tail of Machine Learning","date":"2020-01-21","arxiv_id":"2001.07455","repositories_listed":0,"syntology":null},{"url":null,"slug":"secure-and-robust-machine-learning-for","title":"Secure and Robust Machine Learning for Healthcare: A Survey","date":"2020-01-21","arxiv_id":"2001.08103","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-reuse-with-reduced-kernel-mean","title":"Model Reuse with Reduced Kernel Mean Embedding Specification","date":"2020-01-20","arxiv_id":"2001.07135","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-approach-for-time-aware-domain-based","title":"An Approach for Time-aware Domain-based Social Influence Prediction","date":"2020-01-19","arxiv_id":"2001.07838","repositories_listed":0,"syntology":null},{"url":null,"slug":"infrequent-adverse-event-prediction-in-low","title":"Infrequent adverse event prediction in low carbon energy production using machine learning","date":"2020-01-19","arxiv_id":"2001.06916","repositories_listed":0,"syntology":null},{"url":null,"slug":"pelican-a-deep-residual-network-for-network","title":"Pelican: A Deep Residual Network for Network Intrusion Detection","date":"2020-01-19","arxiv_id":"2001.08523","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-stochastic-optimization","title":"Adaptive Stochastic Optimization","date":"2020-01-18","arxiv_id":"2001.06699","repositories_listed":0,"syntology":null},{"url":null,"slug":"big-data-science-in-porous-materials","title":"Big-Data Science in Porous Materials: Materials Genomics and Machine Learning","date":"2020-01-18","arxiv_id":"2001.06728","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-network-anomalies-using-rule-based","title":"Detecting Network Anomalies using Rule-based machine learning within SNMP-MIB dataset","date":"2020-01-18","arxiv_id":"2002.02368","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecasting-corn-yield-with-machine-learning","title":"Forecasting Corn Yield with Machine Learning Ensembles","date":"2020-01-18","arxiv_id":"2001.09055","repositories_listed":0,"syntology":null},{"url":null,"slug":"intelligent-road-inspection-with-advanced","title":"Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems","date":"2020-01-18","arxiv_id":"2001.08583","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-in-quantitative-pet-imaging","title":"Machine Learning in Quantitative PET Imaging","date":"2020-01-18","arxiv_id":"2001.06597","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inference-of-dynamics-from-partial","title":"Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization","date":"2020-01-17","arxiv_id":"2001.06270","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-permanent-magnet-temperature","title":"Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning","date":"2020-01-17","arxiv_id":"2001.06246","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-and-ai-based-approaches-for","title":"Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition","date":"2020-01-17","arxiv_id":"2001.06545","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-of-statistical-and-machine","title":"Comparison of Statistical and Machine Learning Techniques for Physical Layer Authentication","date":"2020-01-17","arxiv_id":"2001.06238","repositories_listed":0,"syntology":null},{"url":null,"slug":"trust-in-automl-exploring-information-needs","title":"Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems","date":"2020-01-17","arxiv_id":"2001.06509","repositories_listed":0,"syntology":null},{"url":null,"slug":"approximating-trajectory-constraints-with","title":"Approximating Trajectory Constraints with Machine Learning -- Microgrid Islanding with Frequency Constraints","date":"2020-01-16","arxiv_id":"2001.05775","repositories_listed":0,"syntology":null},{"url":null,"slug":"coronary-artery-disease-diagnosis-ranking-the","title":"Coronary Artery Disease Diagnosis; Ranking the Significant Features Using Random Trees Model","date":"2020-01-16","arxiv_id":"2001.09841","repositories_listed":0,"syntology":null},{"url":null,"slug":"elastic-consistency-a-general-consistency","title":"Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent","date":"2020-01-16","arxiv_id":"2001.05918","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-total-cloud-cover","title":"Machine learning for total cloud cover prediction","date":"2020-01-16","arxiv_id":"2001.05948","repositories_listed":0,"syntology":null},{"url":null,"slug":"smart-data-based-ensemble-for-imbalanced-big","title":"Smart Data driven Decision Trees Ensemble Methodology for Imbalanced Big Data","date":"2020-01-16","arxiv_id":"2001.05759","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerated-dual-averaging-primal-dual-method","title":"Accelerated Dual-Averaging Primal-Dual Method for Composite Convex Minimization","date":"2020-01-15","arxiv_id":"2001.05537","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-similarity-measures-from-data","title":"Learning similarity measures from data","date":"2020-01-15","arxiv_id":"2001.05312","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-transfer-efficiencies-for","title":"Machine learning transfer efficiencies for noisy quantum walks","date":"2020-01-15","arxiv_id":"2001.05472","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-content-based-deep-intrusion-detection","title":"A Content-Based Deep Intrusion Detection System","date":"2020-01-14","arxiv_id":"2001.05009","repositories_listed":0,"syntology":null},{"url":null,"slug":"assurance-monitoring-of-cyber-physical","title":"Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components","date":"2020-01-14","arxiv_id":"2001.05014","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentially-private-and-fair","title":"Differentially Private and Fair Classification via Calibrated Functional Mechanism","date":"2020-01-14","arxiv_id":"2001.04958","repositories_listed":0,"syntology":null},{"url":null,"slug":"effects-of-annotation-granularity-in-deep","title":"Effects of annotation granularity in deep learning models for histopathological images","date":"2020-01-14","arxiv_id":"2001.04663","repositories_listed":0,"syntology":null},{"url":null,"slug":"for2for-learning-to-forecast-from-forecasts","title":"For2For: Learning to forecast from forecasts","date":"2020-01-14","arxiv_id":"2001.04601","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-dimensional-brain-in-a-high-dimensional","title":"High--Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality","date":"2020-01-14","arxiv_id":"2001.04959","repositories_listed":0,"syntology":null},{"url":null,"slug":"keeping-community-in-the-loop-understanding","title":"Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems","date":"2020-01-14","arxiv_id":"2001.04879","repositories_listed":0,"syntology":null},{"url":null,"slug":"methodologies-for-successful-segmentation-of","title":"Machine Learning Pipeline for Segmentation and Defect Identification from High Resolution Transmission Electron Microscopy Data","date":"2020-01-14","arxiv_id":"2001.05022","repositories_listed":0,"syntology":null},{"url":null,"slug":"private-machine-learning-via-randomised","title":"Private Machine Learning via Randomised Response","date":"2020-01-14","arxiv_id":"2001.04942","repositories_listed":0,"syntology":null},{"url":null,"slug":"smooth-markets-a-basic-mechanism-for-1","title":"Smooth markets: A basic mechanism for organizing gradient-based learners","date":"2020-01-14","arxiv_id":"2001.04678","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-machine-learning-approach-to-investigate","title":"A machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways","date":"2020-01-13","arxiv_id":"2001.04794","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-machine-learning-based","title":"A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer","date":"2020-01-13","arxiv_id":"2001.04561","repositories_listed":0,"syntology":null},{"url":null,"slug":"consumer-driven-explanations-for-machine","title":"Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness","date":"2020-01-13","arxiv_id":"2001.05573","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-performance-aware","title":"Machine Learning for Performance-Aware Virtual Network Function Placement","date":"2020-01-13","arxiv_id":"2001.07787","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-sensor-data-and-knowledge-fusion-a","title":"Multi-Sensor Data and Knowledge Fusion -- A Proposal for a Terminology Definition","date":"2020-01-13","arxiv_id":"2001.04171","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-automated-swimming-analytics-using","title":"Towards Automated Swimming Analytics Using Deep Neural Networks","date":"2020-01-13","arxiv_id":"2001.04433","repositories_listed":0,"syntology":null},{"url":null,"slug":"channel-assignment-in-uplink-wireless","title":"Channel Assignment in Uplink Wireless Communication using Machine Learning Approach","date":"2020-01-12","arxiv_id":"2001.03952","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-approaches-for-amharic-parts","title":"Machine Learning Approaches for Amharic Parts-of-speech Tagging","date":"2020-01-10","arxiv_id":"2001.03324","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-layer-optimizations-for-end-to-end-data","title":"Multi-layer Optimizations for End-to-End Data Analytics","date":"2020-01-10","arxiv_id":"2001.03541","repositories_listed":0,"syntology":null},{"url":null,"slug":"stock-price-prediction-using-convolutional","title":"Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries","date":"2020-01-10","arxiv_id":"2001.09769","repositories_listed":0,"syntology":null},{"url":null,"slug":"tableqna-answering-list-intent-queries-with","title":"TableQnA: Answering List Intent Queries With Web Tables","date":"2020-01-10","arxiv_id":"2001.04828","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-on-crime-in-denver-city","title":"A Comparative Study on Crime in Denver City Based on Machine Learning and Data Mining","date":"2020-01-09","arxiv_id":"2001.02802","repositories_listed":0,"syntology":null},{"url":null,"slug":"guidelines-for-enhancing-data-locality-in","title":"Guidelines for enhancing data locality in selected machine learning algorithms","date":"2020-01-09","arxiv_id":"2001.03000","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-ergodic-averages-in-chaotic-systems","title":"Learning ergodic averages in chaotic systems","date":"2020-01-09","arxiv_id":"2001.04027","repositories_listed":0,"syntology":null},{"url":null,"slug":"theory-in-theory-out-how-social-theory-can","title":"Theory In, Theory Out: The uses of social theory in machine learning for social science","date":"2020-01-09","arxiv_id":"2001.03203","repositories_listed":0,"syntology":null},{"url":null,"slug":"algorithmic-fairness-from-a-non-ideal","title":"Algorithmic Fairness from a Non-ideal Perspective","date":"2020-01-08","arxiv_id":"2001.09773","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-boosting-on-decision-trees-for","title":"Gradient Boosting on Decision Trees for Mortality Prediction in Transcatheter Aortic Valve Implantation","date":"2020-01-08","arxiv_id":"2001.02431","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-machine-learning-based-plasticity-model","title":"A machine learning based plasticity model using proper orthogonal decomposition","date":"2020-01-07","arxiv_id":"2001.03438","repositories_listed":0,"syntology":null},{"url":null,"slug":"multitask-learning-over-graphs","title":"Multitask learning over graphs: An Approach for Distributed, Streaming Machine Learning","date":"2020-01-07","arxiv_id":"2001.02112","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-of-drug-synergy-by-ensemble","title":"Prediction of Drug Synergy by Ensemble Learning","date":"2020-01-07","arxiv_id":"2001.01997","repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-efficient-neural-networks-for","title":"Resource-Efficient Neural Networks for Embedded Systems","date":"2020-01-07","arxiv_id":"2001.03048","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-data-assimilation-and-machine","title":"Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model","date":"2020-01-06","arxiv_id":"2001.01520","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-event-driven-cameras-for-spatio","title":"Exploiting Event Cameras for Spatio-Temporal Prediction of Fast-Changing Trajectories","date":"2020-01-05","arxiv_id":"2001.01248","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-automatic-threat-detection-a-survey","title":"Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging","date":"2020-01-05","arxiv_id":"2001.01293","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-machine-learning-algorithm-for","title":"Quantum Machine Learning Algorithm for Knowledge Graphs","date":"2020-01-04","arxiv_id":"2001.01077","repositories_listed":0,"syntology":null},{"url":null,"slug":"decomposable-probability-of-success-metrics","title":"Decomposable Probability-of-Success Metrics in Algorithmic Search","date":"2020-01-03","arxiv_id":"2001.00742","repositories_listed":0,"syntology":null},{"url":null,"slug":"fourier-transform-approach-to-machine-1","title":"Fourier Transform Approach to Machine Learning III: Fourier Classification","date":"2020-01-03","arxiv_id":"2001.06081","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-interference-for-counting-clusters","title":"Quantum Interference for Counting Clusters","date":"2020-01-03","arxiv_id":"2001.04251","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-real-world-weight-cross-entropy-loss","title":"The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling","date":"2020-01-03","arxiv_id":"2001.00570","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-loss-function-for-causal-machine-learning","title":"A Loss-Function for Causal Machine-Learning","date":"2020-01-02","arxiv_id":"2001.00629","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-machine-learning-imaging-core-using","title":"A Machine Learning Imaging Core using Separable FIR-IIR Filters","date":"2020-01-02","arxiv_id":"2001.00630","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-signal-processing-part-iii-machine","title":"Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications","date":"2020-01-02","arxiv_id":"2001.00426","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-monoidal-grammars","title":"Incremental Monoidal Grammars","date":"2020-01-02","arxiv_id":"2001.02296","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernelized-support-tensor-train-machines","title":"Kernelized Support Tensor Train Machines","date":"2020-01-02","arxiv_id":"2001.00360","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-consequentialism-and-fairness","title":"On Consequentialism and Fairness","date":"2020-01-02","arxiv_id":"2001.00329","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-machine-learning-insight-through","title":"Visual Machine Learning: Insight through Eigenvectors, Chladni patterns and community detection in 2D particulate structures","date":"2020-01-02","arxiv_id":"2001.00345","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-flexible-framework-for-nonparametric","title":"A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning","date":"2020-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"26ed525a6174d4b5239c9ac776d8857fa3c5a44c71c41a824686a46ff7f96240","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}