{"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/22","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":22,"pages_in_order":101,"rows_per_page":100,"rows":[2101,2200],"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/21","next":"/task/machine-learning/papers/23","papers":[{"url":"/paper/modeling-mistrust-in-end-of-life-care","slug":"modeling-mistrust-in-end-of-life-care","title":"Modeling Mistrust in End-of-Life Care","date":"2018-06-30","arxiv_id":"1807.00124","repositories_listed":1,"syntology":null},{"url":"/paper/measuring-the-quality-of-synthetic-data-for","slug":"measuring-the-quality-of-synthetic-data-for","title":"Measuring the quality of Synthetic data for use in competitions","date":"2018-06-29","arxiv_id":"1806.11345","repositories_listed":1,"syntology":null},{"url":"/paper/restricted-boltzmann-machines-introduction","slug":"restricted-boltzmann-machines-introduction","title":"Restricted Boltzmann Machines: Introduction and Review","date":"2018-06-19","arxiv_id":"1806.07066","repositories_listed":1,"syntology":null},{"url":"/paper/turbulence-correction-with-artificial-neural","slug":"turbulence-correction-with-artificial-neural","title":"Turbulence correction with artificial neural networks","date":"2018-06-19","arxiv_id":"1806.07456","repositories_listed":1,"syntology":null},{"url":"/paper/mlpack-3-a-fast-flexible-machine-learning","slug":"mlpack-3-a-fast-flexible-machine-learning","title":"mlpack 3: a fast, flexible machine learning library","date":"2018-06-18","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-hybrid-machine-learning-model-for","slug":"a-novel-hybrid-machine-learning-model-for","title":"A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases","date":"2018-06-17","arxiv_id":"1806.06423","repositories_listed":1,"syntology":null},{"url":"/paper/servenet-a-deep-neural-network-for-web","slug":"servenet-a-deep-neural-network-for-web","title":"ServeNet: A Deep Neural Network for Web Services Classification","date":"2018-06-14","arxiv_id":"1806.05437","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-attacks-on-variational","slug":"adversarial-attacks-on-variational","title":"Adversarial Attacks on Variational Autoencoders","date":"2018-06-12","arxiv_id":"1806.04646","repositories_listed":1,"syntology":null},{"url":"/paper/mission-ultra-large-scale-feature-selection","slug":"mission-ultra-large-scale-feature-selection","title":"MISSION: Ultra Large-Scale Feature Selection using Count-Sketches","date":"2018-06-12","arxiv_id":"1806.04310","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-classification-tasks-on","slug":"deep-learning-for-classification-tasks-on","title":"Deep Learning for Classification Tasks on Geospatial Vector Polygons","date":"2018-06-11","arxiv_id":"1806.03857","repositories_listed":1,"syntology":null},{"url":"/paper/tapas-tricks-to-accelerate-encrypted","slug":"tapas-tricks-to-accelerate-encrypted","title":"TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service","date":"2018-06-09","arxiv_id":"1806.03461","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-differentiable-programming-in-a","slug":"efficient-differentiable-programming-in-a","title":"Efficient Differentiable Programming in a Functional Array-Processing Language","date":"2018-06-06","arxiv_id":"1806.02136","repositories_listed":1,"syntology":null},{"url":"/paper/bindsnet-a-machine-learning-oriented-spiking","slug":"bindsnet-a-machine-learning-oriented-spiking","title":"BindsNET: A machine learning-oriented spiking neural networks library in Python","date":"2018-06-04","arxiv_id":"1806.01423","repositories_listed":1,"syntology":null},{"url":"/paper/distractor-generation-for-multiple-choice","slug":"distractor-generation-for-multiple-choice","title":"Distractor Generation for Multiple Choice Questions Using Learning to Rank","date":"2018-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/explaining-explanations-an-overview-of","slug":"explaining-explanations-an-overview-of","title":"Explaining Explanations: An Overview of Interpretability of Machine Learning","date":"2018-05-31","arxiv_id":"1806.00069","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-spectrum-of-random-features-maps-of","slug":"on-the-spectrum-of-random-features-maps-of","title":"On the Spectrum of Random Features Maps of High Dimensional Data","date":"2018-05-30","arxiv_id":"1805.11916","repositories_listed":1,"syntology":null},{"url":"/paper/zeno-byzantine-suspicious-stochastic-gradient","slug":"zeno-byzantine-suspicious-stochastic-gradient","title":"Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance","date":"2018-05-25","arxiv_id":"1805.10032","repositories_listed":1,"syntology":null},{"url":"/paper/deeplogic-towards-end-to-end-differentiable","slug":"deeplogic-towards-end-to-end-differentiable","title":"DeepLogic: Towards End-to-End Differentiable Logical Reasoning","date":"2018-05-18","arxiv_id":"1805.07433","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deeplogic-towards-end-to-end-differentiable#ran","syntology_url":"https://syntology.ai/paper/1805.07433","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.07433"}},"official":{"repos":["nuric/deeplogic"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/optimizing-for-generalization-in-machine-1","slug":"optimizing-for-generalization-in-machine-1","title":"Optimizing for Generalization in Machine Learning with Cross-Validation Gradients","date":"2018-05-18","arxiv_id":"1805.07072","repositories_listed":1,"syntology":null},{"url":"/paper/attriguard-a-practical-defense-against","slug":"attriguard-a-practical-defense-against","title":"AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning","date":"2018-05-13","arxiv_id":"1805.04810","repositories_listed":1,"syntology":null},{"url":"/paper/an-on-sorting-algorithm-machine-learning-sort","slug":"an-on-sorting-algorithm-machine-learning-sort","title":"An $O(N)$ Sorting Algorithm: Machine Learning Sort","date":"2018-05-11","arxiv_id":"1805.04272","repositories_listed":1,"syntology":null},{"url":"/paper/online-normalizer-calculation-for-softmax","slug":"online-normalizer-calculation-for-softmax","title":"Online normalizer calculation for softmax","date":"2018-05-08","arxiv_id":"1805.02867","repositories_listed":1,"syntology":null},{"url":"/paper/learning-patient-representations-from-text","slug":"learning-patient-representations-from-text","title":"Learning Patient Representations from Text","date":"2018-05-05","arxiv_id":"1805.02096","repositories_listed":1,"syntology":null},{"url":"/paper/claudette-an-automated-detector-of","slug":"claudette-an-automated-detector-of","title":"CLAUDETTE: an Automated Detector of Potentially Unfair Clauses in Online Terms of Service","date":"2018-05-03","arxiv_id":"1805.01217","repositories_listed":1,"syntology":null},{"url":"/paper/images-recipes-retrieval-in-the-cooking","slug":"images-recipes-retrieval-in-the-cooking","title":"Images & Recipes: Retrieval in the cooking context","date":"2018-05-02","arxiv_id":"1805.00900","repositories_listed":1,"syntology":null},{"url":"/paper/two-multilingual-corpora-extracted-from-the","slug":"two-multilingual-corpora-extracted-from-the","title":"Two Multilingual Corpora Extracted from the Tenders Electronic Daily for Machine Learning and Machine Translation Applications.","date":"2018-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/constraining-effective-field-theories-with","slug":"constraining-effective-field-theories-with","title":"Constraining Effective Field Theories with Machine Learning","date":"2018-04-30","arxiv_id":"1805.00013","repositories_listed":1,"syntology":null},{"url":"/paper/cross-modal-retrieval-in-the-cooking-context","slug":"cross-modal-retrieval-in-the-cooking-context","title":"Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings","date":"2018-04-30","arxiv_id":"1804.11146","repositories_listed":1,"syntology":null},{"url":"/paper/machine-learning-for-exam-triage","slug":"machine-learning-for-exam-triage","title":"Machine Learning for Exam Triage","date":"2018-04-30","arxiv_id":"1805.00503","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-learning-with-quantum-enhanced","slug":"supervised-learning-with-quantum-enhanced","title":"Supervised learning with quantum enhanced feature spaces","date":"2018-04-30","arxiv_id":"1804.11326","repositories_listed":1,"syntology":null},{"url":"/paper/where-are-we-now-a-large-benchmark-study-of","slug":"where-are-we-now-a-large-benchmark-study-of","title":"Where are we now? A large benchmark study of recent symbolic regression methods","date":"2018-04-25","arxiv_id":"1804.09331","repositories_listed":1,"syntology":null},{"url":"/paper/developing-a-machine-learning-framework-for","slug":"developing-a-machine-learning-framework-for","title":"Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data","date":"2018-04-24","arxiv_id":"1804.09046","repositories_listed":1,"syntology":null},{"url":"/paper/achievable-information-rates-for-nonlinear","slug":"achievable-information-rates-for-nonlinear","title":"Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning","date":"2018-04-20","arxiv_id":"1804.07675","repositories_listed":1,"syntology":null},{"url":"/paper/mapping-images-to-psychological-similarity","slug":"mapping-images-to-psychological-similarity","title":"Mapping Images to Psychological Similarity Spaces Using Neural Networks","date":"2018-04-20","arxiv_id":"1804.07758","repositories_listed":1,"syntology":null},{"url":"/paper/quantifying-the-visual-concreteness-of-words","slug":"quantifying-the-visual-concreteness-of-words","title":"Quantifying the visual concreteness of words and topics in multimodal datasets","date":"2018-04-18","arxiv_id":"1804.06786","repositories_listed":1,"syntology":null},{"url":"/paper/a-comparison-of-machine-learning-algorithms","slug":"a-comparison-of-machine-learning-algorithms","title":"A Comparison of Machine Learning Algorithms for the Surveillance of Autism Spectrum Disorder","date":"2018-04-17","arxiv_id":"1804.06223","repositories_listed":1,"syntology":null},{"url":"/paper/rafiki-machine-learning-as-an-analytics","slug":"rafiki-machine-learning-as-an-analytics","title":"Rafiki: Machine Learning as an Analytics Service System","date":"2018-04-17","arxiv_id":"1804.06087","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rafiki-machine-learning-as-an-analytics#ran","syntology_url":"https://syntology.ai/paper/1804.06087","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.06087"}},"official":null}},{"url":"/paper/two-player-games-for-efficient-non-convex","slug":"two-player-games-for-efficient-non-convex","title":"Two-Player Games for Efficient Non-Convex Constrained Optimization","date":"2018-04-17","arxiv_id":"1804.06500","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-federated-learning-in-resource","slug":"adaptive-federated-learning-in-resource","title":"Adaptive Federated Learning in Resource Constrained Edge Computing Systems","date":"2018-04-14","arxiv_id":"1804.05271","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/adaptive-federated-learning-in-resource#ran","syntology_url":"https://syntology.ai/paper/1804.05271","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.05271"}},"official":{"repos":["IBM/adaptive-federated-learning"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/manipulating-machine-learning-poisoning","slug":"manipulating-machine-learning-poisoning","title":"Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning","date":"2018-04-01","arxiv_id":"1804.00308","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/manipulating-machine-learning-poisoning#ran","syntology_url":"https://syntology.ai/paper/1804.00308","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.00308"}},"official":{"repos":["jagielski/manip-ml"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/adversarial-attacks-and-defences-competition","slug":"adversarial-attacks-and-defences-competition","title":"Adversarial Attacks and Defences Competition","date":"2018-03-31","arxiv_id":"1804.00097","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/adversarial-attacks-and-defences-competition#ran","syntology_url":"https://syntology.ai/paper/1804.00097","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.00097"}},"official":{"repos":["pfnet-research/nips17-adversarial-attack"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/detection-localisation-and-tracking-of","slug":"detection-localisation-and-tracking-of","title":"Detection, localisation and tracking of pallets using machine learning techniques and 2D range data","date":"2018-03-29","arxiv_id":"1803.11254","repositories_listed":1,"syntology":null},{"url":"/paper/performance-evaluation-and-hyperparameter","slug":"performance-evaluation-and-hyperparameter","title":"Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data","date":"2018-03-29","arxiv_id":"1803.11266","repositories_listed":1,"syntology":null},{"url":"/paper/why-comparing-single-performance-scores-does","slug":"why-comparing-single-performance-scores-does","title":"Why Comparing Single Performance Scores Does Not Allow to Draw Conclusions About Machine Learning Approaches","date":"2018-03-26","arxiv_id":"1803.09578","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/why-comparing-single-performance-scores-does#ran","syntology_url":"https://syntology.ai/paper/1803.09578","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.09578"}},"official":{"repos":["UKPLab/emnlp2017-bilstm-cnn-crf"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/entanglement-guided-architectures-of-machine","slug":"entanglement-guided-architectures-of-machine","title":"Entanglement-guided architectures of machine learning by quantum tensor network","date":"2018-03-24","arxiv_id":"1803.09111","repositories_listed":1,"syntology":null},{"url":"/paper/clustering-to-reduce-spatial-data-set-size","slug":"clustering-to-reduce-spatial-data-set-size","title":"Clustering to Reduce Spatial Data Set Size","date":"2018-03-21","arxiv_id":"1803.08101","repositories_listed":1,"syntology":null},{"url":"/paper/seglearn-a-python-package-for-learning","slug":"seglearn-a-python-package-for-learning","title":"Seglearn: A Python Package for Learning Sequences and Time Series","date":"2018-03-21","arxiv_id":"1803.08118","repositories_listed":1,"syntology":null},{"url":"/paper/mltuner-system-support-for-automatic-machine","slug":"mltuner-system-support-for-automatic-machine","title":"MLtuner: System Support for Automatic Machine Learning Tuning","date":"2018-03-20","arxiv_id":"1803.07445","repositories_listed":1,"syntology":null},{"url":"/paper/applicability-and-interpretation-of-the","slug":"applicability-and-interpretation-of-the","title":"Applicability and interpretation of the deterministic weighted cepstral distance","date":"2018-03-08","arxiv_id":"1803.03104","repositories_listed":1,"syntology":null},{"url":"/paper/deep-super-learner-a-deep-ensemble-for","slug":"deep-super-learner-a-deep-ensemble-for","title":"Deep Super Learner: A Deep Ensemble for Classification Problems","date":"2018-03-06","arxiv_id":"1803.02323","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-hyperparameter-optimization","slug":"stochastic-hyperparameter-optimization","title":"Stochastic Hyperparameter Optimization through Hypernetworks","date":"2018-02-26","arxiv_id":"1802.09419","repositories_listed":1,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/stochastic-hyperparameter-optimization#ran","syntology_url":"https://syntology.ai/paper/1802.09419","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.09419"}},"official":null}},{"url":"/paper/vr-sgd-a-simple-stochastic-variance-reduction","slug":"vr-sgd-a-simple-stochastic-variance-reduction","title":"VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning","date":"2018-02-26","arxiv_id":"1802.09932","repositories_listed":1,"syntology":null},{"url":"/paper/asynchronous-byzantine-machine-learning-the","slug":"asynchronous-byzantine-machine-learning-the","title":"Asynchronous Byzantine Machine Learning (the case of SGD)","date":"2018-02-22","arxiv_id":"1802.07928","repositories_listed":1,"syntology":null},{"url":"/paper/incremental-and-iterative-learning-of-answer","slug":"incremental-and-iterative-learning-of-answer","title":"Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples","date":"2018-02-22","arxiv_id":"1802.07966","repositories_listed":1,"syntology":null},{"url":"/paper/using-data-mining-to-predict-hospital","slug":"using-data-mining-to-predict-hospital","title":"Using Data Mining to Predict Hospital Admissions From the Emergency Department","date":"2018-02-22","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/determining-the-best-classifier-for","slug":"determining-the-best-classifier-for","title":"Determining the best classifier for predicting the value of a boolean field on a blood donor database using genetic algorithms","date":"2018-02-21","arxiv_id":"1802.07756","repositories_listed":1,"syntology":null},{"url":"/paper/manipulating-and-measuring-model","slug":"manipulating-and-measuring-model","title":"Manipulating and Measuring Model Interpretability","date":"2018-02-21","arxiv_id":"1802.07810","repositories_listed":1,"syntology":null},{"url":"/paper/node-centralities-and-classification","slug":"node-centralities-and-classification","title":"Node Centralities and Classification Performance for Characterizing Node Embedding Algorithms","date":"2018-02-18","arxiv_id":"1802.06368","repositories_listed":1,"syntology":null},{"url":"/paper/a-machine-learning-approach-for-virtual-flow","slug":"a-machine-learning-approach-for-virtual-flow","title":"A Machine Learning Approach for Virtual Flow Metering and Forecasting","date":"2018-02-15","arxiv_id":"1802.05698","repositories_listed":1,"syntology":null},{"url":"/paper/customer-segmentation","slug":"customer-segmentation","title":"Customer Segmentation","date":"2018-02-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/putting-a-bug-in-ml-the-moth-olfactory","slug":"putting-a-bug-in-ml-the-moth-olfactory","title":"Putting a bug in ML: The moth olfactory network learns to read MNIST","date":"2018-02-15","arxiv_id":"1802.05405","repositories_listed":1,"syntology":null},{"url":"/paper/simulation-assisted-machine-learning","slug":"simulation-assisted-machine-learning","title":"Simulation assisted machine learning","date":"2018-02-15","arxiv_id":"1802.05688","repositories_listed":1,"syntology":null},{"url":"/paper/authorship-attribution-using-the-chaos-game","slug":"authorship-attribution-using-the-chaos-game","title":"Authorship Attribution Using the Chaos Game Representation","date":"2018-02-14","arxiv_id":"1802.06007","repositories_listed":1,"syntology":null},{"url":"/paper/playerank-data-driven-performance-evaluation","slug":"playerank-data-driven-performance-evaluation","title":"PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach","date":"2018-02-14","arxiv_id":"1802.04987","repositories_listed":1,"syntology":null},{"url":"/paper/databright-towards-a-global-exchange-for","slug":"databright-towards-a-global-exchange-for","title":"DataBright: Towards a Global Exchange for Decentralized Data Ownership and Trusted Computation","date":"2018-02-13","arxiv_id":"1802.04780","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-membership-inferences-on-well","slug":"understanding-membership-inferences-on-well","title":"Understanding Membership Inferences on Well-Generalized Learning Models","date":"2018-02-13","arxiv_id":"1802.04889","repositories_listed":1,"syntology":null},{"url":"/paper/a-fast-proximal-point-method-for-computing","slug":"a-fast-proximal-point-method-for-computing","title":"A Fast Proximal Point Method for Computing Exact Wasserstein Distance","date":"2018-02-12","arxiv_id":"1802.04307","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-the-vector-space-model-sparse","slug":"revisiting-the-vector-space-model-sparse","title":"Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label Classification","date":"2018-02-12","arxiv_id":"1802.03938","repositories_listed":1,"syntology":null},{"url":"/paper/global-model-interpretation-via-recursive","slug":"global-model-interpretation-via-recursive","title":"Global Model Interpretation via Recursive Partitioning","date":"2018-02-11","arxiv_id":"1802.04253","repositories_listed":1,"syntology":null},{"url":"/paper/neural-architecture-search-with-bayesian","slug":"neural-architecture-search-with-bayesian","title":"Neural Architecture Search with Bayesian Optimisation and Optimal Transport","date":"2018-02-11","arxiv_id":"1802.07191","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/neural-architecture-search-with-bayesian#ran","syntology_url":"https://syntology.ai/paper/1802.07191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.07191"}},"official":{"repos":["kirthevasank/nasbot"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/detection-of-adversarial-training-examples-in","slug":"detection-of-adversarial-training-examples-in","title":"Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection","date":"2018-02-08","arxiv_id":"1802.03041","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-evaluation-of-deep-learning-for","slug":"an-empirical-evaluation-of-deep-learning-for","title":"An Empirical Evaluation of Deep Learning for ICD-9 Code Assignment using MIMIC-III Clinical Notes","date":"2018-02-07","arxiv_id":"1802.02311","repositories_listed":1,"syntology":null},{"url":"/paper/detecting-zones-and-threat-on-3d-body-for","slug":"detecting-zones-and-threat-on-3d-body-for","title":"Detecting Zones and Threat on 3D Body for Security in Airports using Deep Machine Learning","date":"2018-02-02","arxiv_id":"1802.00565","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-approach-for-very-similar","slug":"deep-learning-approach-for-very-similar","title":"Deep Learning Approach for Very Similar Objects Recognition Application on Chihuahua and Muffin Problem","date":"2018-01-29","arxiv_id":"1801.09573","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-approaches-for-supervised-semantic","slug":"evaluating-approaches-for-supervised-semantic","title":"Evaluating approaches for supervised semantic labeling","date":"2018-01-29","arxiv_id":"1801.09788","repositories_listed":1,"syntology":null},{"url":"/paper/malaria-detection-using-image-processing-and","slug":"malaria-detection-using-image-processing-and","title":"Malaria Detection Using Image Processing and Machine Learning","date":"2018-01-28","arxiv_id":"1801.10031","repositories_listed":1,"syntology":null},{"url":"/paper/gradient-descent-revisited-via-an-adaptive","slug":"gradient-descent-revisited-via-an-adaptive","title":"Gradient descent revisited via an adaptive online learning rate","date":"2018-01-27","arxiv_id":"1801.09136","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-sentiment-analysis-a-survey","slug":"deep-learning-for-sentiment-analysis-a-survey","title":"Deep Learning for Sentiment Analysis : A Survey","date":"2018-01-24","arxiv_id":"1801.07883","repositories_listed":1,"syntology":null},{"url":"/paper/layered-tpot-speeding-up-tree-based-pipeline","slug":"layered-tpot-speeding-up-tree-based-pipeline","title":"Layered TPOT: Speeding up Tree-based Pipeline Optimization","date":"2018-01-18","arxiv_id":"1801.06007","repositories_listed":1,"syntology":null},{"url":"/paper/a-human-grounded-evaluation-benchmark-for","slug":"a-human-grounded-evaluation-benchmark-for","title":"A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning","date":"2018-01-16","arxiv_id":"1801.05075","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-movie-genres-based-on-plot","slug":"predicting-movie-genres-based-on-plot","title":"Predicting Movie Genres Based on Plot Summaries","date":"2018-01-15","arxiv_id":"1801.04813","repositories_listed":1,"syntology":null},{"url":"/paper/shielding-googles-language-toxicity-model","slug":"shielding-googles-language-toxicity-model","title":"Shielding Google's language toxicity model against adversarial attacks","date":"2018-01-05","arxiv_id":"1801.01828","repositories_listed":1,"syntology":null},{"url":"/paper/query-limited-black-box-attacks-to","slug":"query-limited-black-box-attacks-to","title":"Query-limited Black-box Attacks to Classifiers","date":"2017-12-23","arxiv_id":"1712.08713","repositories_listed":1,"syntology":null},{"url":"/paper/an-mpi-based-python-framework-for-distributed","slug":"an-mpi-based-python-framework-for-distributed","title":"An MPI-Based Python Framework for Distributed Training with Keras","date":"2017-12-16","arxiv_id":"1712.05878","repositories_listed":1,"syntology":null},{"url":"/paper/rasa-open-source-language-understanding-and","slug":"rasa-open-source-language-understanding-and","title":"Rasa: Open Source Language Understanding and Dialogue Management","date":"2017-12-14","arxiv_id":"1712.05181","repositories_listed":1,"syntology":null},{"url":"/paper/ballpark-crowdsourcing-the-wisdom-of-rough","slug":"ballpark-crowdsourcing-the-wisdom-of-rough","title":"Ballpark Crowdsourcing: The Wisdom of Rough Group Comparisons","date":"2017-12-13","arxiv_id":"1712.04828","repositories_listed":1,"syntology":null},{"url":"/paper/improving-malware-detection-accuracy-by","slug":"improving-malware-detection-accuracy-by","title":"Improving Malware Detection Accuracy by Extracting Icon Information","date":"2017-12-10","arxiv_id":"1712.03483","repositories_listed":1,"syntology":null},{"url":"/paper/best-response-regression","slug":"best-response-regression","title":"Best Response Regression","date":"2017-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/assessing-information-transmission-in-data","slug":"assessing-information-transmission-in-data","title":"Assessing Information Transmission in Data Transformations with the Channel Multivariate Entropy Triangle","date":"2017-11-30","arxiv_id":"1711.11510","repositories_listed":1,"syntology":null},{"url":"/paper/introduction-to-tensor-decompositions-and","slug":"introduction-to-tensor-decompositions-and","title":"Introduction to Tensor Decompositions and their Applications in Machine Learning","date":"2017-11-29","arxiv_id":"1711.10781","repositories_listed":1,"syntology":null},{"url":"/paper/emfet-e-mail-features-extraction-tool","slug":"emfet-e-mail-features-extraction-tool","title":"EMFET: E-mail Features Extraction Tool","date":"2017-11-22","arxiv_id":"1711.08521","repositories_listed":1,"syntology":null},{"url":"/paper/using-stochastic-computation-graphs-formalism","slug":"using-stochastic-computation-graphs-formalism","title":"Using stochastic computation graphs formalism for optimization of sequence-to-sequence model","date":"2017-11-21","arxiv_id":"1711.07724","repositories_listed":1,"syntology":null},{"url":"/paper/on-breast-cancer-detection-an-application-of","slug":"on-breast-cancer-detection-an-application-of","title":"On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset","date":"2017-11-20","arxiv_id":"1711.07831","repositories_listed":1,"syntology":null},{"url":"/paper/an-accelerated-communication-efficient-primal","slug":"an-accelerated-communication-efficient-primal","title":"An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine Learning","date":"2017-11-14","arxiv_id":"1711.05305","repositories_listed":1,"syntology":null},{"url":"/paper/guided-machine-learning-for-power-grid","slug":"guided-machine-learning-for-power-grid","title":"Guided Machine Learning for power grid segmentation","date":"2017-11-13","arxiv_id":"1711.09715","repositories_listed":1,"syntology":null},{"url":"/paper/classifierguesser-a-context-based-classifier","slug":"classifierguesser-a-context-based-classifier","title":"ClassifierGuesser: A Context-based Classifier Prediction System for Chinese Language Learners","date":"2017-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/orthogonal-machine-learning-power-and","slug":"orthogonal-machine-learning-power-and","title":"Orthogonal Machine Learning: Power and Limitations","date":"2017-11-01","arxiv_id":"1711.00342","repositories_listed":1,"syntology":null},{"url":"/paper/jsut-corpus-free-large-scale-japanese-speech","slug":"jsut-corpus-free-large-scale-japanese-speech","title":"JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis","date":"2017-10-28","arxiv_id":"1711.00354","repositories_listed":1,"syntology":null},{"url":"/paper/powered-outer-probabilistic-clustering","slug":"powered-outer-probabilistic-clustering","title":"Powered Outer Probabilistic Clustering","date":"2017-10-25","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/benchmark-of-deep-learning-models-on-large","slug":"benchmark-of-deep-learning-models-on-large","title":"Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets","date":"2017-10-23","arxiv_id":"1710.08531","repositories_listed":1,"syntology":null}],"record_sha256":"9f30ac7ec5aae3b522eabc170d824a83344894a4c34191389a293776b3bfbdbc","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}