{"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/53","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":53,"pages_in_order":101,"rows_per_page":100,"rows":[5201,5300],"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/52","next":"/task/machine-learning/papers/54","papers":[{"url":null,"slug":"value-cards-an-educational-toolkit-for","title":"Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation","date":"2020-10-22","arxiv_id":"2010.11411","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-level-wise-taxonomic-perspective-on","title":"AutoML to Date and Beyond: Challenges and Opportunities","date":"2020-10-21","arxiv_id":"2010.10777","repositories_listed":0,"syntology":null},{"url":null,"slug":"amnesiac-machine-learning","title":"Amnesiac Machine Learning","date":"2020-10-21","arxiv_id":"2010.10981","repositories_listed":0,"syntology":null},{"url":"/paper/gender-prediction-based-on-vietnamese-names","slug":"gender-prediction-based-on-vietnamese-names","title":"Gender Prediction Based on Vietnamese Names with Machine Learning Techniques","date":"2020-10-21","arxiv_id":"2010.10852","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-role-of-machine-learning-for-trajectory","title":"The Role of Machine Learning for Trajectory Prediction in Cooperative Driving","date":"2020-10-21","arxiv_id":"2010.11743","repositories_listed":0,"syntology":null},{"url":null,"slug":"american-sign-language-identification-using","title":"American Sign Language Identification Using Hand Trackpoint Analysis","date":"2020-10-20","arxiv_id":"2010.10590","repositories_listed":0,"syntology":null},{"url":null,"slug":"counterfactual-explanations-for-machine-1","title":"Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review","date":"2020-10-20","arxiv_id":"2010.10596","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-specific-data-subsampling-with","title":"Model-specific Data Subsampling with Influence Functions","date":"2020-10-20","arxiv_id":"2010.10218","repositories_listed":0,"syntology":null},{"url":null,"slug":"preventing-personal-data-theft-in-images-with","title":"Ulixes: Facial Recognition Privacy with Adversarial Machine Learning","date":"2020-10-20","arxiv_id":"2010.10242","repositories_listed":0,"syntology":null},{"url":null,"slug":"dos-and-don-ts-of-machine-learning-in","title":"Dos and Don'ts of Machine Learning in Computer Security","date":"2020-10-19","arxiv_id":"2010.09470","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-machine-learning-a-brief","title":"Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges","date":"2020-10-19","arxiv_id":"2010.09337","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-evaluation-of-the-echo","title":"Machine Learning Evaluation of the Echo-Chamber Effect in Medical Forums","date":"2020-10-19","arxiv_id":"2010.09574","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-decision-lists-using-sat","title":"Optimal Decision Lists using SAT","date":"2020-10-19","arxiv_id":"2010.09919","repositories_listed":0,"syntology":null},{"url":null,"slug":"smarttriage-a-system-for-personalized-patient","title":"SmartTriage: A system for personalized patient data capture, documentation generation, and decision support","date":"2020-10-19","arxiv_id":"2010.09905","repositories_listed":0,"syntology":null},{"url":null,"slug":"sniper-gmms-structured-gaussian-mixtures","title":"Sniper GMMs: Structured Gaussian mixtures poison ML on large n small p data with high efficacy","date":"2020-10-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"survey-on-causal-based-machine-learning","title":"Survey on Causal-based Machine Learning Fairness Notions","date":"2020-10-19","arxiv_id":"2010.09553","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-based-automated-species-identification","title":"Image-based Automated Species Identification: Can Virtual Data Augmentation Overcome Problems of Insufficient Sampling?","date":"2020-10-18","arxiv_id":"2010.09009","repositories_listed":0,"syntology":null},{"url":null,"slug":"living-in-the-physics-and-machine-learning","title":"Living in the Physics and Machine Learning Interplay for Earth Observation","date":"2020-10-18","arxiv_id":"2010.09031","repositories_listed":0,"syntology":null},{"url":null,"slug":"noma-in-uav-aided-cellular-offloading-a","title":"NOMA in UAV-aided cellular offloading: A machine learning approach","date":"2020-10-18","arxiv_id":"2011.14776","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generative-model-based-adversarial-security","title":"A Generative Model based Adversarial Security of Deep Learning and Linear Classifier Models","date":"2020-10-17","arxiv_id":"2010.08546","repositories_listed":0,"syntology":null},{"url":null,"slug":"mlcask-efficient-management-of-component","title":"MLCask: Efficient Management of Component Evolution in Collaborative Data Analytics Pipelines","date":"2020-10-17","arxiv_id":"2010.10246","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-machine-learning-to-reduce-ensembles-of","title":"Using machine learning to reduce ensembles of geological models for oil and gas exploration","date":"2020-10-17","arxiv_id":"2010.08775","repositories_listed":0,"syntology":null},{"url":null,"slug":"virtual-savant-learning-for-optimization","title":"Virtual Savant: learning for optimization","date":"2020-10-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generalizable-and-accessible-approach-to","title":"A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery","date":"2020-10-16","arxiv_id":"2010.08168","repositories_listed":0,"syntology":null},{"url":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","repositories_listed":0,"syntology":null},{"url":null,"slug":"evidential-reasoning-with-expert-guided","title":"Evidential Reasoning with Expert-Guided Machine Learning","date":"2020-10-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizable-machine-learning-in","title":"Generalizable Machine Learning in Neuroscience using Graph Neural Networks","date":"2020-10-16","arxiv_id":"2010.08569","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-powered-mitigation-policy","title":"Machine Learning-Powered Mitigation Policy Optimization in Epidemiological Models","date":"2020-10-16","arxiv_id":"2010.08478","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-evaluation-and-application-of","title":"Performance evaluation and application of computation based low-cost homogeneous machine learning model algorithm for image classification","date":"2020-10-16","arxiv_id":"2010.08087","repositories_listed":0,"syntology":null},{"url":null,"slug":"smart-grid-a-survey-of-architectural-elements","title":"Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions","date":"2020-10-16","arxiv_id":"2010.08094","repositories_listed":0,"syntology":null},{"url":null,"slug":"closed-loop-neural-interfaces-with-embedded","title":"Closed-Loop Neural Interfaces with Embedded Machine Learning","date":"2020-10-15","arxiv_id":"2010.09457","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-models-for-predicting-wildfires","title":"Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data","date":"2020-10-15","arxiv_id":"2010.07445","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-neural-network-predictions-for","title":"Explaining Neural Network Predictions for Functional Data Using Principal Component Analysis and Feature Importance","date":"2020-10-15","arxiv_id":"2010.12063","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-industrial-safety","title":"Machine learning for industrial safety culture in the developing world","date":"2020-10-15","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sobolev-training-of-thermodynamic-informed","title":"Sobolev training of thermodynamic-informed neural networks for smoothed elasto-plasticity models with level set hardening","date":"2020-10-15","arxiv_id":"2010.11265","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-design-and-development-of-games-with-a","title":"The Design and Development of Games with a Purpose for AI Systems","date":"2020-10-15","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-shift-to-6g-communications-vision-and","title":"The Shift to 6G Communications: Vision and Requirements","date":"2020-10-15","arxiv_id":"2010.07993","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-based-active-learning-strategy","title":"Uncertainty Based Active Learning Strategy for Interactive Weakly Supervised Learning through Data Programming","date":"2020-10-15","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-introduction-to-electrocatalyst-design","title":"An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage","date":"2020-10-14","arxiv_id":"2010.09435","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainability-for-fair-machine-learning-1","title":"Explainability for fair machine learning","date":"2020-10-14","arxiv_id":"2010.07389","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-force-fields","title":"Machine Learning Force Fields","date":"2020-10-14","arxiv_id":"2010.07067","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-the-originality-of-intellectual","title":"Measuring the originality of intellectual property assets based on machine learning outputs","date":"2020-10-14","arxiv_id":"2010.06997","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-aided-radio-user-identity-match-in","title":"Vision-Aided Radio: User Identity Match in Radio and Video Domains Using Machine Learning","date":"2020-10-14","arxiv_id":"2010.07219","repositories_listed":0,"syntology":null},{"url":null,"slug":"covid-19-imaging-data-privacy-by-federated","title":"COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework","date":"2020-10-13","arxiv_id":"2010.06177","repositories_listed":0,"syntology":null},{"url":null,"slug":"credit-card-fraud-detection-using-machine","title":"Credit card fraud detection using machine learning: A survey","date":"2020-10-13","arxiv_id":"2010.06479","repositories_listed":0,"syntology":null},{"url":null,"slug":"fantastic-features-and-where-to-find-them","title":"Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach","date":"2020-10-13","arxiv_id":"2010.06579","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-networks-a-practical-approach","title":"Language Networks: a Practical Approach","date":"2020-10-13","arxiv_id":"2010.06710","repositories_listed":0,"syntology":null},{"url":null,"slug":"s3ml-a-secure-serving-system-for-machine","title":"S3ML: A Secure Serving System for Machine Learning Inference","date":"2020-10-13","arxiv_id":"2010.06212","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-is-enough-enough-just-enough-decision","title":"When is Enough Enough? \"Just Enough\" Decision Making with Recurrent Neural Networks for Radio Frequency Machine Learning","date":"2020-10-13","arxiv_id":"2010.06352","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-song-contest-human-ai-co-creation-in","title":"AI Song Contest: Human-AI Co-Creation in Songwriting","date":"2020-10-12","arxiv_id":"2010.05388","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-information-theoretic-perspective-on","title":"An Information-Theoretic Perspective on Overfitting and Underfitting","date":"2020-10-12","arxiv_id":"2010.06076","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-machine-learning-and-mechanism","title":"Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness","date":"2020-10-12","arxiv_id":"2010.05434","repositories_listed":0,"syntology":null},{"url":null,"slug":"escalation-prediction-using-feature","title":"Escalation Prediction using Feature Engineering: Addressing Support Ticket Escalations within IBM's Ecosystem","date":"2020-10-12","arxiv_id":"2010.06390","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-material","title":"Machine Learning for Material Characterization with an Application for Predicting Mechanical Properties","date":"2020-10-12","arxiv_id":"2010.06010","repositories_listed":0,"syntology":null},{"url":null,"slug":"monitoring-war-destruction-from-space-a","title":"Monitoring War Destruction from Space: A Machine Learning Approach","date":"2020-10-12","arxiv_id":"2010.05970","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-finite-temperature-kohn-sham","title":"Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks","date":"2020-10-10","arxiv_id":"2010.04905","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-representation-learning-a","title":"Contrastive Representation Learning: A Framework and Review","date":"2020-10-10","arxiv_id":"2010.05113","repositories_listed":0,"syntology":null},{"url":null,"slug":"hamlet-a-hierarchical-agent-based-machine","title":"HAMLET: A Hierarchical Agent-based Machine Learning Platform","date":"2020-10-10","arxiv_id":"2010.04894","repositories_listed":0,"syntology":null},{"url":null,"slug":"k-simplex2vec-a-simplicial-extension-of-1","title":"$k$-simplex2vec: a simplicial extension of node2vec","date":"2020-10-10","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rare-event-simulation-for-neural-network-and","title":"Rare-Event Simulation for Neural Network and Random Forest Predictors","date":"2020-10-10","arxiv_id":"2010.04890","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-series-of-unfortunate-counterfactual-events","title":"A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations","date":"2020-10-09","arxiv_id":"2010.04687","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-approach-to-muon","title":"Machine Learning approach to muon spectroscopy analysis","date":"2020-10-09","arxiv_id":"2010.04742","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-exploration-with-cost-aware-learning","title":"Model Exploration with Cost-Aware Learning","date":"2020-10-09","arxiv_id":"2010.04512","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-regulating-ai-in-medical-products-onramp","title":"OnRAMP for Regulating AI in Medical Products","date":"2020-10-09","arxiv_id":"2010.07038","repositories_listed":0,"syntology":null},{"url":null,"slug":"prognosis-prediction-in-covid-19-patients","title":"Prognosis Prediction in Covid-19 Patients from Lab Tests and X-ray Data through Randomized Decision Trees","date":"2020-10-09","arxiv_id":"2010.04420","repositories_listed":0,"syntology":null},{"url":null,"slug":"thermal-aware-compilation-of-spiking-neural","title":"Thermal-Aware Compilation of Spiking Neural Networks to Neuromorphic Hardware","date":"2020-10-09","arxiv_id":"2010.04773","repositories_listed":0,"syntology":null},{"url":null,"slug":"wildfire-smoke-and-air-quality-how-machine","title":"Wildfire Smoke and Air Quality: How Machine Learning Can Guide Forest Management","date":"2020-10-09","arxiv_id":"2010.04651","repositories_listed":0,"syntology":null},{"url":null,"slug":"affine-invariant-robust-training","title":"Affine-Invariant Robust Training","date":"2020-10-08","arxiv_id":"2010.04216","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-sensitivity-of-icf-outputs-to","title":"Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine Learning","date":"2020-10-08","arxiv_id":"2010.04254","repositories_listed":0,"syntology":null},{"url":null,"slug":"metrics-and-methods-for-a-systematic","title":"Metrics and methods for a systematic comparison of fairness-aware machine learning algorithms","date":"2020-10-08","arxiv_id":"2010.03986","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-brief-review-of-domain-adaptation","title":"A Brief Review of Domain Adaptation","date":"2020-10-07","arxiv_id":"2010.03978","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-and-efficient-tensor-calculus-for","title":"A Simple and Efficient Tensor Calculus for Machine Learning","date":"2020-10-07","arxiv_id":"2010.03313","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-attacks-to-machine-learning-based","title":"Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems","date":"2020-10-07","arxiv_id":"2010.03671","repositories_listed":0,"syntology":null},{"url":null,"slug":"correlated-differential-privacy-feature","title":"Correlated Differential Privacy: Feature Selection in Machine Learning","date":"2020-10-07","arxiv_id":"2010.03094","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-non-i-i-d-data-towards-more-robust","title":"Exploiting non-i.i.d. data towards more robust machine learning algorithms","date":"2020-10-07","arxiv_id":"2010.03429","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpreting-imagined-speech-waves-with","title":"Interpreting Imagined Speech Waves with Machine Learning techniques","date":"2020-10-07","arxiv_id":"2010.03360","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-recovery-factor","title":"Machine learning for recovery factor estimation of an oil reservoir: a tool for de-risking at a hydrocarbon asset evaluation","date":"2020-10-07","arxiv_id":"2010.03408","repositories_listed":0,"syntology":null},{"url":null,"slug":"not-all-datasets-are-born-equal-on","title":"Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples","date":"2020-10-07","arxiv_id":"2010.03180","repositories_listed":0,"syntology":null},{"url":null,"slug":"physical-system-for-non-time-sequence-data","title":"Physical System for Non Time Sequence Data","date":"2020-10-07","arxiv_id":"2010.03206","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconfigurable-intelligent-surfaces-and","title":"Reconfigurable Intelligent Surfaces and Machine Learning for Wireless Fingerprinting Localization","date":"2020-10-07","arxiv_id":"2010.03251","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-note-on-high-probability-versus-in","title":"A Note on High-Probability versus In-Expectation Guarantees of Generalization Bounds in Machine Learning","date":"2020-10-06","arxiv_id":"2010.02576","repositories_listed":0,"syntology":null},{"url":null,"slug":"baaan-backdoor-attacks-against-autoencoder","title":"BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models","date":"2020-10-06","arxiv_id":"2010.03007","repositories_listed":0,"syntology":null},{"url":null,"slug":"chess-as-a-testing-grounds-for-the-oracle","title":"Chess as a Testing Grounds for the Oracle Approach to AI Safety","date":"2020-10-06","arxiv_id":"2010.02911","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-network-an-efficient-and","title":"Deep Neural Network: An Efficient and Optimized Machine Learning Paradigm for Reducing Genome Sequencing Error","date":"2020-10-06","arxiv_id":"2010.03420","repositories_listed":0,"syntology":null},{"url":null,"slug":"downscaling-attacks-what-you-see-is-not-what","title":"Downscaling Attack and Defense: Turning What You See Back Into What You Get","date":"2020-10-06","arxiv_id":"2010.02456","repositories_listed":0,"syntology":null},{"url":null,"slug":"learn-to-synchronize-synchronize-to-learn","title":"Learn to Synchronize, Synchronize to Learn","date":"2020-10-06","arxiv_id":"2010.02860","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-empowered-trajectory-and","title":"Machine Learning Empowered Trajectory and Passive Beamforming Design in UAV-RIS Wireless Networks","date":"2020-10-06","arxiv_id":"2010.02749","repositories_listed":0,"syntology":null},{"url":null,"slug":"pcal-a-privacy-preserving-intelligent-credit","title":"PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning","date":"2020-10-06","arxiv_id":"2010.02529","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-of-vegetation-encroachment-detection","title":"A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery","date":"2020-10-05","arxiv_id":"2010.01757","repositories_listed":0,"syntology":null},{"url":null,"slug":"global-soil-moisture-from-in-situ","title":"Global soil moisture from in-situ measurements using machine learning -- SoMo.ml","date":"2020-10-05","arxiv_id":"2010.02374","repositories_listed":0,"syntology":null},{"url":null,"slug":"leapme-learning-based-property-matching-with","title":"LEAPME: Learning-based Property Matching with Embeddings","date":"2020-10-05","arxiv_id":"2010.01951","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-manifold-implicitly-via-explicit","title":"Learning Manifold Implicitly via Explicit Heat-Kernel Learning","date":"2020-10-05","arxiv_id":"2010.01761","repositories_listed":0,"syntology":null},{"url":null,"slug":"metadata-based-detection-of-child-sexual","title":"Metadata-Based Detection of Child Sexual Abuse Material","date":"2020-10-05","arxiv_id":"2010.02387","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generative-machine-learning-approach-to","title":"A Generative Machine Learning Approach to Policy Optimization in Pursuit-Evasion Games","date":"2020-10-04","arxiv_id":"2010.01711","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-primer-on-model-guided-exploration-of","title":"A primer on model-guided exploration of fitness landscapes for biological sequence design","date":"2020-10-04","arxiv_id":"2010.10614","repositories_listed":0,"syntology":null},{"url":null,"slug":"diagonal-memory-optimisation-for-machine","title":"Diagonal Memory Optimisation for Machine Learning on Micro-controllers","date":"2020-10-04","arxiv_id":"2010.01668","repositories_listed":0,"syntology":null},{"url":null,"slug":"dns-covert-channel-detection-via-behavioral","title":"DNS Covert Channel Detection via Behavioral Analysis: a Machine Learning Approach","date":"2020-10-04","arxiv_id":"2010.01582","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-machine-learning-methods-for","title":"Ensemble Machine Learning Methods for Modeling COVID19 Deaths","date":"2020-10-04","arxiv_id":"2010.04052","repositories_listed":0,"syntology":null},{"url":null,"slug":"entity-embedding-as-game-representation","title":"Entity Embedding as Game Representation","date":"2020-10-04","arxiv_id":"2010.01685","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainability-via-responsibility","title":"Explainability via Responsibility","date":"2020-10-04","arxiv_id":"2010.01676","repositories_listed":0,"syntology":null}],"record_sha256":"5ccae891f476547656e7460c57f2f59ed8f5c1e4df042753325eec440f8c53ab","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}