{"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/14","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":14,"pages_in_order":101,"rows_per_page":100,"rows":[1301,1400],"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/13","next":"/task/machine-learning/papers/15","papers":[{"url":"/paper/correlator-convolutional-neural-networks-an","slug":"correlator-convolutional-neural-networks-an","title":"Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data","date":"2020-11-06","arxiv_id":"2011.03474","repositories_listed":1,"syntology":null},{"url":"/paper/applying-machine-learning-to-crowd-sourced","slug":"applying-machine-learning-to-crowd-sourced","title":"Applying Machine Learning to Crowd-sourced Data from Earthquake Detective","date":"2020-11-05","arxiv_id":"2011.04740","repositories_listed":1,"syntology":null},{"url":"/paper/deep-generative-selection-models-of-t-and-b","slug":"deep-generative-selection-models-of-t-and-b","title":"Deep generative selection models of T and B cell receptor repertoires with soNNia","date":"2020-11-05","arxiv_id":"2011.03112","repositories_listed":1,"syntology":null},{"url":"/paper/comparison-of-pharmacist-evaluation-of","slug":"comparison-of-pharmacist-evaluation-of","title":"Comparison of pharmacist evaluation of medication orders with predictions of a machine learning model","date":"2020-11-03","arxiv_id":"2011.01925","repositories_listed":1,"syntology":null},{"url":"/paper/power-of-data-in-quantum-machine-learning","slug":"power-of-data-in-quantum-machine-learning","title":"Power of data in quantum machine learning","date":"2020-11-03","arxiv_id":"2011.01938","repositories_listed":1,"syntology":null},{"url":"/paper/results-of-a-single-blind-literary-taste-test","slug":"results-of-a-single-blind-literary-taste-test","title":"Results of a Single Blind Literary Taste Test with Short Anonymized Novel Fragments","date":"2020-11-03","arxiv_id":"2011.01624","repositories_listed":1,"syntology":null},{"url":"/paper/vega-towards-an-end-to-end-configurable","slug":"vega-towards-an-end-to-end-configurable","title":"VEGA: Towards an End-to-End Configurable AutoML Pipeline","date":"2020-11-03","arxiv_id":"2011.01507","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-annotator-bias-with-a-graph","slug":"investigating-annotator-bias-with-a-graph","title":"Investigating Annotator Bias with a Graph-Based Approach","date":"2020-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/optsla-an-optimization-based-approach-for","slug":"optsla-an-optimization-based-approach-for","title":"OptSLA: an Optimization-Based Approach for Sequential Label Aggregation","date":"2020-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/identifying-exoplanets-with-deep-learning-iv","slug":"identifying-exoplanets-with-deep-learning-iv","title":"Identifying Exoplanets with Deep Learning. IV. Removing Stellar Activity Signals from Radial Velocity Measurements Using Neural Networks","date":"2020-10-30","arxiv_id":"2011.00003","repositories_listed":1,"syntology":null},{"url":"/paper/fact-or-factitious-contextualized-opinion-1","slug":"fact-or-factitious-contextualized-opinion-1","title":"Fact or Factitious? Contextualized Opinion Spam Detection","date":"2020-10-29","arxiv_id":"2010.15296","repositories_listed":1,"syntology":null},{"url":"/paper/how-many-pages-paper-length-prediction-from","slug":"how-many-pages-paper-length-prediction-from","title":"How Many Pages? Paper Length Prediction from the Metadata","date":"2020-10-29","arxiv_id":"2010.15924","repositories_listed":1,"syntology":null},{"url":"/paper/generating-knowledge-graphs-by-employing","slug":"generating-knowledge-graphs-by-employing","title":"Generating Knowledge Graphs by Employing Natural Language Processing and Machine Learning Techniques within the Scholarly Domain","date":"2020-10-28","arxiv_id":"2011.01103","repositories_listed":1,"syntology":null},{"url":"/paper/image-representations-learned-with","slug":"image-representations-learned-with","title":"Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases","date":"2020-10-28","arxiv_id":"2010.15052","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learning-of-solvent-effects-on","slug":"machine-learning-of-solvent-effects-on","title":"Machine learning of solvent effects on molecular spectra and reactions","date":"2020-10-28","arxiv_id":"2010.14942","repositories_listed":1,"syntology":null},{"url":"/paper/speech-based-emotion-recognition-using-neural","slug":"speech-based-emotion-recognition-using-neural","title":"Speech-Based Emotion Recognition using Neural Networks and Information Visualization","date":"2020-10-28","arxiv_id":"2010.15229","repositories_listed":1,"syntology":null},{"url":"/paper/fit-to-measure-reasoning-about-sizes-for","slug":"fit-to-measure-reasoning-about-sizes-for","title":"Fit to Measure: Reasoning about Sizes for Robust Object Recognition","date":"2020-10-27","arxiv_id":"2010.14296","repositories_listed":1,"syntology":null},{"url":"/paper/physics-based-deep-learning-for-fiber-optic","slug":"physics-based-deep-learning-for-fiber-optic","title":"Physics-Based Deep Learning for Fiber-Optic Communication Systems","date":"2020-10-27","arxiv_id":"2010.14258","repositories_listed":1,"syntology":null},{"url":"/paper/clrgaze-contrastive-learning-of","slug":"clrgaze-contrastive-learning-of","title":"CLRGaze: Contrastive Learning of Representations for Eye Movement Signals","date":"2020-10-25","arxiv_id":"2010.13046","repositories_listed":1,"syntology":null},{"url":"/paper/eegsig-machine-learning-based-toolbox-for-end","slug":"eegsig-machine-learning-based-toolbox-for-end","title":"EEGsig: an open-source machine learning-based toolbox for EEG signal processing","date":"2020-10-24","arxiv_id":"2010.12877","repositories_listed":1,"syntology":null},{"url":"/paper/gradient-flows-in-dataset-space","slug":"gradient-flows-in-dataset-space","title":"Dataset Dynamics via Gradient Flows in Probability Space","date":"2020-10-24","arxiv_id":"2010.12760","repositories_listed":1,"syntology":{"n":22,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/gradient-flows-in-dataset-space#ran","syntology_url":"https://syntology.ai/paper/2010.12760","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12760"}},"official":null}},{"url":"/paper/differentially-private-learning-does-not","slug":"differentially-private-learning-does-not","title":"Investigating Membership Inference Attacks under Data Dependencies","date":"2020-10-23","arxiv_id":"2010.12112","repositories_listed":1,"syntology":null},{"url":"/paper/fairput-a-light-framework-for-machine","slug":"fairput-a-light-framework-for-machine","title":"FairPut: A Light Framework for Machine Learning Fairness with LightGBM","date":"2020-10-22","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-augmented-energy-minimization-via","slug":"learning-augmented-energy-minimization-via","title":"Learning Augmented Energy Minimization via Speed Scaling","date":"2020-10-22","arxiv_id":"2010.11629","repositories_listed":1,"syntology":{"n":5,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 5 unverified","sample_list":"/paper/learning-augmented-energy-minimization-via#ran","syntology_url":"https://syntology.ai/paper/2010.11629","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.11629"}},"official":{"repos":["andreasr27/LAS"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":[]}}},{"url":"/paper/object-attribute-biclustering-for-elimination","slug":"object-attribute-biclustering-for-elimination","title":"Object-Attribute Biclustering for Elimination of Missing Genotypes in Ischemic Stroke Genome-Wide Data","date":"2020-10-22","arxiv_id":"2010.11641","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-of-machine-learning-techniques-in","slug":"a-survey-of-machine-learning-techniques-in","title":"A Survey of Machine Learning Techniques in Adversarial Image Forensics","date":"2020-10-19","arxiv_id":"2010.09680","repositories_listed":1,"syntology":null},{"url":"/paper/against-all-odds-winning-the-defense","slug":"against-all-odds-winning-the-defense","title":"Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification","date":"2020-10-19","arxiv_id":"2010.09569","repositories_listed":1,"syntology":null},{"url":"/paper/connections-between-relational-event-model","slug":"connections-between-relational-event-model","title":"Connections between Relational Event Model and Inverse Reinforcement Learning for Characterizing Group Interaction Sequences","date":"2020-10-19","arxiv_id":"2010.09810","repositories_listed":1,"syntology":null},{"url":"/paper/dan-an-optimal-data-assimilation-framework","slug":"dan-an-optimal-data-assimilation-framework","title":"Data Assimilation Networks","date":"2020-10-19","arxiv_id":"2010.09694","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-linear-solvers-for-machine","slug":"probabilistic-linear-solvers-for-machine","title":"Probabilistic Linear Solvers for Machine Learning","date":"2020-10-19","arxiv_id":"2010.09691","repositories_listed":1,"syntology":null},{"url":"/paper/when-bots-take-over-the-stock-market-evasion","slug":"when-bots-take-over-the-stock-market-evasion","title":"Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders","date":"2020-10-19","arxiv_id":"2010.09246","repositories_listed":1,"syntology":null},{"url":"/paper/difer-differentiable-automated-feature","slug":"difer-differentiable-automated-feature","title":"DIFER: Differentiable Automated Feature Engineering","date":"2020-10-17","arxiv_id":"2010.08784","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-information-processing-in-human","slug":"understanding-information-processing-in-human","title":"Understanding Information Processing in Human Brain by Interpreting Machine Learning Models","date":"2020-10-17","arxiv_id":"2010.08715","repositories_listed":1,"syntology":null},{"url":"/paper/altruist-argumentative-explanations-through","slug":"altruist-argumentative-explanations-through","title":"Altruist: Argumentative Explanations through Local Interpretations of Predictive Models","date":"2020-10-15","arxiv_id":"2010.07650","repositories_listed":1,"syntology":null},{"url":"/paper/what-you-need-to-know-to-train-recurrent","slug":"what-you-need-to-know-to-train-recurrent","title":"Exploring Flip Flop memories and beyond: training recurrent neural networks with key insights","date":"2020-10-15","arxiv_id":"2010.07858","repositories_listed":1,"syntology":null},{"url":"/paper/domain-shift-in-computer-vision-models-for","slug":"domain-shift-in-computer-vision-models-for","title":"Domain Shift in Computer Vision models for MRI data analysis: An Overview","date":"2020-10-14","arxiv_id":"2010.07222","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-in-streaming-submodular-maximization","slug":"fairness-in-streaming-submodular-maximization","title":"Fairness in Streaming Submodular Maximization: Algorithms and Hardness","date":"2020-10-14","arxiv_id":"2010.07431","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-machine-learning-with-an","slug":"interpretable-machine-learning-with-an","title":"Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines","date":"2020-10-14","arxiv_id":"2010.07388","repositories_listed":1,"syntology":null},{"url":"/paper/mycorrhiza-genotype-assignment","slug":"mycorrhiza-genotype-assignment","title":"Mycorrhiza: Genotype Assignment usingPhylogenetic Networks","date":"2020-10-14","arxiv_id":"2010.09483","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-preserving-object-detection","slug":"privacy-preserving-object-detection","title":"Privacy-Preserving Object Detection & Localization Using Distributed Machine Learning: A Case Study of Infant Eyeblink Conditioning","date":"2020-10-14","arxiv_id":"2010.07259","repositories_listed":1,"syntology":null},{"url":"/paper/annotationsaurus-a-searchable-directory-of","slug":"annotationsaurus-a-searchable-directory-of","title":"Annotationsaurus: A Searchable Directory of Annotation Tools","date":"2020-10-13","arxiv_id":"2010.06251","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-tree-explanation-methods-for","slug":"evaluating-tree-explanation-methods-for","title":"Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter","date":"2020-10-13","arxiv_id":"2010.06734","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learning-for-the-diagnosis-of","slug":"machine-learning-for-the-diagnosis-of","title":"Machine learning for the diagnosis of Parkinson's disease: A systematic review","date":"2020-10-13","arxiv_id":"2010.06101","repositories_listed":1,"syntology":null},{"url":"/paper/active-learning-with-resspect-resource","slug":"active-learning-with-resspect-resource","title":"Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients","date":"2020-10-12","arxiv_id":"2010.05941","repositories_listed":1,"syntology":null},{"url":"/paper/garfield-system-support-for-byzantine-machine","slug":"garfield-system-support-for-byzantine-machine","title":"Garfield: System Support for Byzantine Machine Learning","date":"2020-10-12","arxiv_id":"2010.05888","repositories_listed":1,"syntology":null},{"url":"/paper/k-simplex2vec-a-simplicial-extension-of","slug":"k-simplex2vec-a-simplicial-extension-of","title":"k-simplex2vec: a simplicial extension of node2vec","date":"2020-10-12","arxiv_id":"2010.05636","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_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) · 0 unverified","sample_list":"/paper/k-simplex2vec-a-simplicial-extension-of#ran","syntology_url":"https://syntology.ai/paper/2010.05636","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.05636"}},"official":null}},{"url":"/paper/on-feature-selection-using-anisotropic","slug":"on-feature-selection-using-anisotropic","title":"On Feature Selection Using Anisotropic General Regression Neural Network","date":"2020-10-12","arxiv_id":"2010.05744","repositories_listed":1,"syntology":null},{"url":"/paper/an-open-review-of-openreview-a-critical-1","slug":"an-open-review-of-openreview-a-critical-1","title":"An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Process","date":"2020-10-11","arxiv_id":"2010.05137","repositories_listed":1,"syntology":null},{"url":"/paper/lambda-learner-fast-incremental-learning-on","slug":"lambda-learner-fast-incremental-learning-on","title":"Lambda Learner: Fast Incremental Learning on Data Streams","date":"2020-10-11","arxiv_id":"2010.05154","repositories_listed":1,"syntology":null},{"url":"/paper/a-tensor-compiler-for-unified-machine","slug":"a-tensor-compiler-for-unified-machine","title":"A Tensor Compiler for Unified Machine Learning Prediction Serving","date":"2020-10-09","arxiv_id":"2010.04804","repositories_listed":1,"syntology":null},{"url":"/paper/large-scale-randomized-experiment-reveals","slug":"large-scale-randomized-experiment-reveals","title":"Large-scale randomized experiment reveals machine learning helps people learn and remember more effectively","date":"2020-10-09","arxiv_id":"2010.04430","repositories_listed":1,"syntology":null},{"url":"/paper/scaling-systematic-literature-reviews-with","slug":"scaling-systematic-literature-reviews-with","title":"Scaling Systematic Literature Reviews with Machine Learning Pipelines","date":"2020-10-09","arxiv_id":"2010.04665","repositories_listed":1,"syntology":null},{"url":"/paper/learning-nonlinear-dynamics-and-chaos-a","slug":"learning-nonlinear-dynamics-and-chaos-a","title":"Knowledge-Based Learning of Nonlinear Dynamics and Chaos","date":"2020-10-07","arxiv_id":"2010.03415","repositories_listed":1,"syntology":null},{"url":"/paper/tail-risk-protection-machine-learning-meets","slug":"tail-risk-protection-machine-learning-meets","title":"Tail-risk protection: Machine Learning meets modern Econometrics","date":"2020-10-07","arxiv_id":"2010.03315","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-attackable-sentences-in-arguments","slug":"detecting-attackable-sentences-in-arguments","title":"Detecting Attackable Sentences in Arguments","date":"2020-10-06","arxiv_id":"2010.02660","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/detecting-attackable-sentences-in-arguments#ran","syntology_url":"https://syntology.ai/paper/2010.02660","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.02660"}},"official":{"repos":["yohanjo/emnlp20_arg_attack"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-computation-of-contrastive","slug":"efficient-computation-of-contrastive","title":"Efficient computation of contrastive explanations","date":"2020-10-06","arxiv_id":"2010.02647","repositories_listed":1,"syntology":null},{"url":"/paper/erfit-entropic-regression-fit-matlab-package","slug":"erfit-entropic-regression-fit-matlab-package","title":"ERFit: Entropic Regression Fit Matlab Package, for Data-Driven System Identification of Underlying Dynamic Equations","date":"2020-10-06","arxiv_id":"2010.02411","repositories_listed":1,"syntology":null},{"url":"/paper/logan-local-group-bias-detection-by","slug":"logan-local-group-bias-detection-by","title":"LOGAN: Local Group Bias Detection by Clustering","date":"2020-10-06","arxiv_id":"2010.02867","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learned-preconditioners-for-linear","slug":"machine-learned-preconditioners-for-linear","title":"Machine-Learned Preconditioners for Linear Solvers in Geophysical Fluid Flows","date":"2020-10-06","arxiv_id":"2010.02866","repositories_listed":1,"syntology":null},{"url":"/paper/a-rigorous-and-robust-quantum-speed-up-in","slug":"a-rigorous-and-robust-quantum-speed-up-in","title":"A rigorous and robust quantum speed-up in supervised machine learning","date":"2020-10-05","arxiv_id":"2010.02174","repositories_listed":1,"syntology":null},{"url":"/paper/faultnet-a-deep-convolutional-neural-network","slug":"faultnet-a-deep-convolutional-neural-network","title":"FaultNet: A Deep Convolutional Neural Network for bearing fault classification","date":"2020-10-05","arxiv_id":"2010.02146","repositories_listed":1,"syntology":null},{"url":"/paper/instead-of-rewriting-foreign-code-for-machine","slug":"instead-of-rewriting-foreign-code-for-machine","title":"Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients","date":"2020-10-04","arxiv_id":"2010.01709","repositories_listed":1,"syntology":null},{"url":"/paper/doubleensemble-a-new-ensemble-method-based-on","slug":"doubleensemble-a-new-ensemble-method-based-on","title":"DoubleEnsemble: A New Ensemble Method Based on Sample Reweighting and Feature Selection for Financial Data Analysis","date":"2020-10-03","arxiv_id":"2010.01265","repositories_listed":1,"syntology":null},{"url":"/paper/attention-based-clustering-learning-a-kernel","slug":"attention-based-clustering-learning-a-kernel","title":"Attention-Based Clustering: Learning a Kernel from Context","date":"2020-10-02","arxiv_id":"2010.01040","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-the-role-of-momentum-in-non","slug":"understanding-the-role-of-momentum-in-non","title":"Momentum via Primal Averaging: Theoretical Insights and Learning Rate Schedules for Non-Convex Optimization","date":"2020-10-01","arxiv_id":"2010.00406","repositories_listed":1,"syntology":null},{"url":"/paper/a-supervised-machine-learning-approach-for","slug":"a-supervised-machine-learning-approach-for","title":"A Supervised Machine Learning Approach for Accelerating the Design of Particulate Composites: Application to Thermal Conductivity","date":"2020-09-30","arxiv_id":"2010.00041","repositories_listed":1,"syntology":null},{"url":"/paper/first-order-optimization-for-superquantile","slug":"first-order-optimization-for-superquantile","title":"First-order Optimization for Superquantile-based Supervised Learning","date":"2020-09-30","arxiv_id":"2009.14575","repositories_listed":1,"syntology":null},{"url":"/paper/is-ai-model-interpretable-to-combat-with","slug":"is-ai-model-interpretable-to-combat-with","title":"Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task","date":"2020-09-30","arxiv_id":"2010.02006","repositories_listed":1,"syntology":null},{"url":"/paper/afro-mnist-synthetic-generation-of-mnist","slug":"afro-mnist-synthetic-generation-of-mnist","title":"Afro-MNIST: Synthetic generation of MNIST-style datasets for low-resource languages","date":"2020-09-28","arxiv_id":"2009.13509","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learning-of-partial-differential","slug":"machine-learning-of-partial-differential","title":"Machine Learning of Partial Differential Equations from Noise Data","date":"2020-09-28","arxiv_id":"2010.06507","repositories_listed":1,"syntology":null},{"url":"/paper/transparency-auditability-and-explainability","slug":"transparency-auditability-and-explainability","title":"Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring","date":"2020-09-28","arxiv_id":"2009.13384","repositories_listed":1,"syntology":null},{"url":"/paper/high-definition-image-classification-in","slug":"high-definition-image-classification-in","title":"High Definition image classification in Geoscience using Machine Learning","date":"2020-09-25","arxiv_id":"2010.03965","repositories_listed":1,"syntology":null},{"url":"/paper/secure-data-sharing-with-flow-model","slug":"secure-data-sharing-with-flow-model","title":"Secure Data Sharing With Flow Model","date":"2020-09-24","arxiv_id":"2009.11762","repositories_listed":1,"syntology":null},{"url":"/paper/dataset-optimization-strategies-for","slug":"dataset-optimization-strategies-for","title":"Dataset Optimization Strategies for MalwareTraffic Detection","date":"2020-09-23","arxiv_id":"2009.11347","repositories_listed":1,"syntology":null},{"url":"/paper/from-things-modeling-language-thingml-to","slug":"from-things-modeling-language-thingml-to","title":"From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)","date":"2020-09-22","arxiv_id":"2009.10632","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-workflows-and-machine-learning-for","slug":"semantic-workflows-and-machine-learning-for","title":"Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees","date":"2020-09-22","arxiv_id":"2009.10263","repositories_listed":1,"syntology":null},{"url":"/paper/thingml-augmenting-model-driven-software","slug":"thingml-augmenting-model-driven-software","title":"ThingML+ Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning","date":"2020-09-22","arxiv_id":"2009.10633","repositories_listed":1,"syntology":null},{"url":"/paper/deap-cache-deep-eviction-admission-and","slug":"deap-cache-deep-eviction-admission-and","title":"DEAP Cache: Deep Eviction Admission and Prefetching for Cache","date":"2020-09-19","arxiv_id":"2009.09206","repositories_listed":1,"syntology":null},{"url":"/paper/kohn-sham-equations-as-regularizer-building","slug":"kohn-sham-equations-as-regularizer-building","title":"Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics","date":"2020-09-17","arxiv_id":"2009.08551","repositories_listed":1,"syntology":null},{"url":"/paper/automated-seismic-source-characterisation","slug":"automated-seismic-source-characterisation","title":"Automated Seismic Source Characterisation Using Deep Graph Neural Networks","date":"2020-09-16","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/m-arcsinh-an-efficient-and-reliable-function","slug":"m-arcsinh-an-efficient-and-reliable-function","title":"m-arcsinh: An Efficient and Reliable Function for SVM and MLP in scikit-learn","date":"2020-09-16","arxiv_id":"2009.07530","repositories_listed":1,"syntology":null},{"url":"/paper/malicious-network-traffic-detection-via-deep","slug":"malicious-network-traffic-detection-via-deep","title":"Malicious Network Traffic Detection via Deep Learning: An Information Theoretic View","date":"2020-09-16","arxiv_id":"2009.07753","repositories_listed":1,"syntology":null},{"url":"/paper/analyzing-koopman-approaches-to-physics","slug":"analyzing-koopman-approaches-to-physics","title":"Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forecasting","date":"2020-09-15","arxiv_id":"2010.00399","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/analyzing-koopman-approaches-to-physics#ran","syntology_url":"https://syntology.ai/paper/2010.00399","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.00399"}},"official":{"repos":["JRice15/physics-informed-autoencoders"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/reviewviz-assisting-developers-perform","slug":"reviewviz-assisting-developers-perform","title":"ReviewViz: Assisting Developers Perform Empirical Study on Energy Consumption Related Reviews for Mobile Applications","date":"2020-09-13","arxiv_id":"2009.06027","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-machine-learning-approaches-to","slug":"interpretable-machine-learning-approaches-to","title":"Interpretable Machine Learning Approaches to Prediction of Chronic Homelessness","date":"2020-09-12","arxiv_id":"2009.09072","repositories_listed":1,"syntology":null},{"url":"/paper/melime-meaningful-local-explanation-for","slug":"melime-meaningful-local-explanation-for","title":"MeLIME: Meaningful Local Explanation for Machine Learning Models","date":"2020-09-12","arxiv_id":"2009.05818","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learning-based-edfa-gain-model","slug":"machine-learning-based-edfa-gain-model","title":"Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices","date":"2020-09-11","arxiv_id":"2009.05326","repositories_listed":1,"syntology":null},{"url":"/paper/p-critical-a-reservoir-autoregulation","slug":"p-critical-a-reservoir-autoregulation","title":"P-CRITICAL: A Reservoir Autoregulation Plasticity Rule for Neuromorphic Hardware","date":"2020-09-11","arxiv_id":"2009.05593","repositories_listed":1,"syntology":null},{"url":"/paper/power-evolution-prediction-and-optimization","slug":"power-evolution-prediction-and-optimization","title":"Power Evolution Prediction and Optimization in a Multi-span System Based on Component-wise System Modeling","date":"2020-09-11","arxiv_id":"2009.05348","repositories_listed":1,"syntology":null},{"url":"/paper/an-experimentally-driven-automated-machine","slug":"an-experimentally-driven-automated-machine","title":"An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide","date":"2020-09-09","arxiv_id":"2009.04045","repositories_listed":1,"syntology":null},{"url":"/paper/beneficial-and-harmful-explanatory-machine","slug":"beneficial-and-harmful-explanatory-machine","title":"Beneficial and Harmful Explanatory Machine Learning","date":"2020-09-09","arxiv_id":"2009.06410","repositories_listed":1,"syntology":null},{"url":"/paper/neural-time-dependent-partial-differential","slug":"neural-time-dependent-partial-differential","title":"Neural-PDE: A RNN based neural network for solving time dependent PDEs","date":"2020-09-08","arxiv_id":"2009.03892","repositories_listed":1,"syntology":null},{"url":"/paper/deepsun-machine-learning-as-a-service-for","slug":"deepsun-machine-learning-as-a-service-for","title":"DeepSun: Machine-Learning-as-a-Service for Solar Flare Prediction","date":"2020-09-04","arxiv_id":"2009.04238","repositories_listed":1,"syntology":null},{"url":"/paper/simulation-assisted-decorrelation-for","slug":"simulation-assisted-decorrelation-for","title":"Simulation-Assisted Decorrelation for Resonant Anomaly Detection","date":"2020-09-04","arxiv_id":"2009.02205","repositories_listed":1,"syntology":null},{"url":"/paper/adaboost-cnn-an-adaptive-boosting-algorithm","slug":"adaboost-cnn-an-adaptive-boosting-algorithm","title":"AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning","date":"2020-09-03","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/p6-a-declarative-language-for-integrating","slug":"p6-a-declarative-language-for-integrating","title":"P6: A Declarative Language for Integrating Machine Learning in Visual Analytics","date":"2020-09-03","arxiv_id":"2009.01399","repositories_listed":1,"syntology":null},{"url":"/paper/process-mining-meets-causal-machine-learning","slug":"process-mining-meets-causal-machine-learning","title":"Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs","date":"2020-09-03","arxiv_id":"2009.01561","repositories_listed":1,"syntology":null},{"url":"/paper/smoke-testing-for-machine-learning-simple","slug":"smoke-testing-for-machine-learning-simple","title":"Smoke Testing for Machine Learning: Simple Tests to Discover Severe Defects","date":"2020-09-03","arxiv_id":"2009.01521","repositories_listed":1,"syntology":null},{"url":"/paper/tree-neural-networks-in-hol4","slug":"tree-neural-networks-in-hol4","title":"Tree Neural Networks in HOL4","date":"2020-09-03","arxiv_id":"2009.01827","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-of-machine-learning-algorithms-for-1","slug":"evaluation-of-machine-learning-algorithms-for-1","title":"Evaluation of machine learning algorithms for Health and Wellness applications: a tutorial","date":"2020-08-31","arxiv_id":"2008.13690","repositories_listed":1,"syntology":null}],"record_sha256":"75a43c448bfb24ed9f391d66ecfccf1395cd9f484998da8bd98b67c89614afe6","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}