{"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/11","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":11,"pages_in_order":101,"rows_per_page":100,"rows":[1001,1100],"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/10","next":"/task/machine-learning/papers/12","papers":[{"url":"/paper/plant-disease-detection-using-image","slug":"plant-disease-detection-using-image","title":"Plant Disease Detection Using Image Processing and Machine Learning","date":"2021-06-20","arxiv_id":"2106.10698","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-on-machine-learning-algorithms-for","slug":"a-survey-on-machine-learning-algorithms-for","title":"A Survey on Machine Learning Algorithms for Applications in Cognitive Radio Networks","date":"2021-06-19","arxiv_id":"2106.10413","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-learning-vector-quantization-for","slug":"generalized-learning-vector-quantization-for","title":"Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing","date":"2021-06-17","arxiv_id":"2106.09821","repositories_listed":1,"syntology":null},{"url":"/paper/pykale-knowledge-aware-machine-learning-from","slug":"pykale-knowledge-aware-machine-learning-from","title":"PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python","date":"2021-06-17","arxiv_id":"2106.09756","repositories_listed":1,"syntology":null},{"url":"/paper/comparison-of-automated-machine-learning","slug":"comparison-of-automated-machine-learning","title":"Comparison of Automated Machine Learning Tools for SMS Spam Message Filtering","date":"2021-06-16","arxiv_id":"2106.08671","repositories_listed":1,"syntology":null},{"url":"/paper/developing-a-fidelity-evaluation-approach-for","slug":"developing-a-fidelity-evaluation-approach-for","title":"Developing a Fidelity Evaluation Approach for Interpretable Machine Learning","date":"2021-06-16","arxiv_id":"2106.08492","repositories_listed":1,"syntology":null},{"url":"/paper/deep-reinforcement-learning-for-conservation","slug":"deep-reinforcement-learning-for-conservation","title":"Deep Reinforcement Learning for Conservation Decisions","date":"2021-06-15","arxiv_id":"2106.08272","repositories_listed":1,"syntology":null},{"url":"/paper/backdoor-learning-curves-explaining-backdoor","slug":"backdoor-learning-curves-explaining-backdoor","title":"Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions","date":"2021-06-14","arxiv_id":"2106.07214","repositories_listed":1,"syntology":null},{"url":"/paper/launching-into-clinical-space-with-medspacy-a","slug":"launching-into-clinical-space-with-medspacy-a","title":"Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python","date":"2021-06-14","arxiv_id":"2106.07799","repositories_listed":1,"syntology":null},{"url":"/paper/variational-quanvolutional-neural-networks","slug":"variational-quanvolutional-neural-networks","title":"Variational Quanvolutional Neural Networks with enhanced image encoding","date":"2021-06-14","arxiv_id":"2106.07327","repositories_listed":1,"syntology":null},{"url":"/paper/autoscore-survival-developing-interpretable","slug":"autoscore-survival-developing-interpretable","title":"AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data","date":"2021-06-13","arxiv_id":"2106.06957","repositories_listed":1,"syntology":null},{"url":"/paper/reducing-effects-of-swath-gaps-on","slug":"reducing-effects-of-swath-gaps-on","title":"Reducing Effects of Swath Gaps on Unsupervised Machine Learning Models for NASA MODIS Instruments","date":"2021-06-13","arxiv_id":"2106.07113","repositories_listed":1,"syntology":null},{"url":"/paper/the-backpropagation-algorithm-implemented-on","slug":"the-backpropagation-algorithm-implemented-on","title":"The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware","date":"2021-06-13","arxiv_id":"2106.07030","repositories_listed":1,"syntology":null},{"url":"/paper/twitter-sentiment-analysis-1","slug":"twitter-sentiment-analysis-1","title":"Twitter Sentiment Analysis","date":"2021-06-13","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/solving-pdes-on-unknown-manifolds-with","slug":"solving-pdes-on-unknown-manifolds-with","title":"Solving PDEs on Unknown Manifolds with Machine Learning","date":"2021-06-12","arxiv_id":"2106.06682","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-enhanced-machine-learning-pipeline","slug":"knowledge-enhanced-machine-learning-pipeline","title":"Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks","date":"2021-06-11","arxiv_id":"2106.06235","repositories_listed":1,"syntology":null},{"url":"/paper/wax-ml-a-python-library-for-machine-learning","slug":"wax-ml-a-python-library-for-machine-learning","title":"WAX-ML: A Python library for machine learning and feedback loops on streaming data","date":"2021-06-11","arxiv_id":"2106.06524","repositories_listed":1,"syntology":null},{"url":"/paper/explaining-time-series-predictions-with","slug":"explaining-time-series-predictions-with","title":"Explaining Time Series Predictions with Dynamic Masks","date":"2021-06-09","arxiv_id":"2106.05303","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/explaining-time-series-predictions-with#ran","syntology_url":"https://syntology.ai/paper/2106.05303","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.05303"}},"official":{"repos":["JonathanCrabbe/Dynamask"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/neighborhood-contrastive-learning-applied-to","slug":"neighborhood-contrastive-learning-applied-to","title":"Neighborhood Contrastive Learning Applied to Online Patient Monitoring","date":"2021-06-09","arxiv_id":"2106.05142","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/neighborhood-contrastive-learning-applied-to#ran","syntology_url":"https://syntology.ai/paper/2106.05142","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.05142"}},"official":{"repos":["ratschlab/ncl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/streambrain-an-hpc-framework-for-brain-like","slug":"streambrain-an-hpc-framework-for-brain-like","title":"StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs","date":"2021-06-09","arxiv_id":"2106.05373","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-generation-of-machine-learning","slug":"automatic-generation-of-machine-learning","title":"Automatic Generation of Machine Learning Synthetic Data Using ROS","date":"2021-06-08","arxiv_id":"2106.04547","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-role-of-feedback-in-visual-processing","slug":"on-the-role-of-feedback-in-visual-processing","title":"On the role of feedback in visual processing: a predictive coding perspective","date":"2021-06-08","arxiv_id":"2106.04225","repositories_listed":1,"syntology":null},{"url":"/paper/hybrid-machine-learning-forecasts-for-the-1","slug":"hybrid-machine-learning-forecasts-for-the-1","title":"Hybrid Machine Learning Forecasts for the UEFA EURO 2020","date":"2021-06-07","arxiv_id":"2106.05799","repositories_listed":1,"syntology":null},{"url":"/paper/occode-an-end-to-end-machine-learning","slug":"occode-an-end-to-end-machine-learning","title":"Lessons learned developing and using a machine learning model to automatically transcribe 2.3 million handwritten occupation codes","date":"2021-06-07","arxiv_id":"2106.03996","repositories_listed":1,"syntology":null},{"url":"/paper/widening-access-to-applied-machine-learning","slug":"widening-access-to-applied-machine-learning","title":"Widening Access to Applied Machine Learning with TinyML","date":"2021-06-07","arxiv_id":"2106.04008","repositories_listed":1,"syntology":null},{"url":"/paper/a-near-optimal-algorithm-for-debiasing","slug":"a-near-optimal-algorithm-for-debiasing","title":"A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models","date":"2021-06-06","arxiv_id":"2106.12887","repositories_listed":1,"syntology":null},{"url":"/paper/deep-particulate-matter-forecasting-model","slug":"deep-particulate-matter-forecasting-model","title":"Deep Particulate Matter Forecasting Model Using Correntropy-Induced Loss","date":"2021-06-06","arxiv_id":"2106.03032","repositories_listed":1,"syntology":null},{"url":"/paper/learning-proofs-for-the-classification-of","slug":"learning-proofs-for-the-classification-of","title":"Learning proofs for the classification of nilpotent semigroups","date":"2021-06-06","arxiv_id":"2106.03015","repositories_listed":1,"syntology":null},{"url":"/paper/a-new-gastric-histopathology-subsize-image","slug":"a-new-gastric-histopathology-subsize-image","title":"GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer","date":"2021-06-04","arxiv_id":"2106.02473","repositories_listed":1,"syntology":null},{"url":"/paper/extreme-sparsity-gives-rise-to-functional","slug":"extreme-sparsity-gives-rise-to-functional","title":"Dynamics of specialization in neural modules under resource constraints","date":"2021-06-04","arxiv_id":"2106.02626","repositories_listed":1,"syntology":null},{"url":"/paper/learning-curves-for-sgd-on-structured","slug":"learning-curves-for-sgd-on-structured","title":"Learning Curves for SGD on Structured Features","date":"2021-06-04","arxiv_id":"2106.02713","repositories_listed":1,"syntology":null},{"url":"/paper/neuracrypt-hiding-private-health-data-via","slug":"neuracrypt-hiding-private-health-data-via","title":"NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training","date":"2021-06-04","arxiv_id":"2106.02484","repositories_listed":1,"syntology":null},{"url":"/paper/out-of-distribution-generalization-in-kernel","slug":"out-of-distribution-generalization-in-kernel","title":"Out-of-Distribution Generalization in Kernel Regression","date":"2021-06-04","arxiv_id":"2106.02261","repositories_listed":1,"syntology":null},{"url":"/paper/spreadgnn-serverless-multi-task-federated","slug":"spreadgnn-serverless-multi-task-federated","title":"SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks","date":"2021-06-04","arxiv_id":"2106.02743","repositories_listed":1,"syntology":null},{"url":"/paper/ukiyo-e-analysis-and-creativity-with","slug":"ukiyo-e-analysis-and-creativity-with","title":"Ukiyo-e Analysis and Creativity with Attribute and Geometry Annotation","date":"2021-06-04","arxiv_id":"2106.02267","repositories_listed":1,"syntology":null},{"url":"/paper/bifair-training-fair-models-with-bilevel","slug":"bifair-training-fair-models-with-bilevel","title":"Fair Machine Learning under Limited Demographically Labeled Data","date":"2021-06-03","arxiv_id":"2106.04757","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/bifair-training-fair-models-with-bilevel#ran","syntology_url":"https://syntology.ai/paper/2106.04757","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04757"}},"official":{"repos":["TinfoilHat0/BiFair"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/machine-learning-models-for-dota-2-outcomes","slug":"machine-learning-models-for-dota-2-outcomes","title":"Machine learning models for DOTA 2 outcomes prediction","date":"2021-06-03","arxiv_id":"2106.01782","repositories_listed":1,"syntology":null},{"url":"/paper/solving-schrodinger-bridges-via-maximum","slug":"solving-schrodinger-bridges-via-maximum","title":"Solving Schrödinger Bridges via Maximum Likelihood","date":"2021-06-03","arxiv_id":"2106.02081","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/solving-schrodinger-bridges-via-maximum#ran","syntology_url":"https://syntology.ai/paper/2106.02081","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.02081"}},"official":{"repos":["franciscovargas/GP_Sinkhorn"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-for-network-traffic","slug":"deep-learning-for-network-traffic","title":"Deep Learning for Network Traffic Classification","date":"2021-06-02","arxiv_id":"2106.12693","repositories_listed":1,"syntology":null},{"url":"/paper/fair-preprocessing-towards-understanding","slug":"fair-preprocessing-towards-understanding","title":"Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline","date":"2021-06-02","arxiv_id":"2106.06054","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fair-preprocessing-towards-understanding#ran","syntology_url":"https://syntology.ai/paper/2106.06054","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06054"}},"official":{"repos":["sumonbis/FairPreprocessing"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/is-it-a-click-bait-let-s-predict-using","slug":"is-it-a-click-bait-let-s-predict-using","title":"Is it a click bait? Let's predict using Machine Learning","date":"2021-06-01","arxiv_id":"2106.07348","repositories_listed":1,"syntology":null},{"url":"/paper/markpainting-adversarial-machine-learning","slug":"markpainting-adversarial-machine-learning","title":"Markpainting: Adversarial Machine Learning meets Inpainting","date":"2021-06-01","arxiv_id":"2106.00660","repositories_listed":1,"syntology":null},{"url":"/paper/restoring-the-sister-reconstructing-a-lexicon","slug":"restoring-the-sister-reconstructing-a-lexicon","title":"Restoring the Sister: Reconstructing a Lexicon from Sister Languages using Neural Machine Translation","date":"2021-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/the-zoo-of-fairness-metrics-in-machine","slug":"the-zoo-of-fairness-metrics-in-machine","title":"A Clarification of the Nuances in the Fairness Metrics Landscape","date":"2021-06-01","arxiv_id":"2106.00467","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-peacefulness-through-the-world","slug":"understanding-peacefulness-through-the-world","title":"Understanding peacefulness through the world news","date":"2021-06-01","arxiv_id":"2106.00306","repositories_listed":1,"syntology":null},{"url":"/paper/quantum-federated-learning-with-quantum-data","slug":"quantum-federated-learning-with-quantum-data","title":"Quantum Federated Learning with Quantum Data","date":"2021-05-30","arxiv_id":"2106.00005","repositories_listed":1,"syntology":null},{"url":"/paper/fit-without-fear-remarkable-mathematical","slug":"fit-without-fear-remarkable-mathematical","title":"Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation","date":"2021-05-29","arxiv_id":"2105.14368","repositories_listed":1,"syntology":null},{"url":"/paper/ten-quick-tips-for-deep-learning-in-biology","slug":"ten-quick-tips-for-deep-learning-in-biology","title":"Ten Quick Tips for Deep Learning in Biology","date":"2021-05-29","arxiv_id":"2105.14372","repositories_listed":1,"syntology":null},{"url":"/paper/autonomous-optimization-of-fluid-systems-at","slug":"autonomous-optimization-of-fluid-systems-at","title":"A Machine Learning and Computer Vision Approach to Rapidly Optimize Multiscale Droplet Generation","date":"2021-05-28","arxiv_id":"2105.13553","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/autonomous-optimization-of-fluid-systems-at#ran","syntology_url":"https://syntology.ai/paper/2105.13553","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.13553"}},"official":{"repos":["pv-lab/ml-multiscale-droplets"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/shell-theory-a-statistical-model-of-reality","slug":"shell-theory-a-statistical-model-of-reality","title":"Shell Theory: A Statistical Model of Reality","date":"2021-05-28","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/robust-learning-from-corrupted-eeg-with","slug":"robust-learning-from-corrupted-eeg-with","title":"Robust learning from corrupted EEG with dynamic spatial filtering","date":"2021-05-27","arxiv_id":"2105.12916","repositories_listed":1,"syntology":null},{"url":"/paper/an-explainable-probabilistic-classifier-for","slug":"an-explainable-probabilistic-classifier-for","title":"An Explainable Probabilistic Classifier for Categorical Data Inspired to Quantum Physics","date":"2021-05-26","arxiv_id":"2105.13988","repositories_listed":1,"syntology":null},{"url":"/paper/pytouch-a-machine-learning-library-for-touch","slug":"pytouch-a-machine-learning-library-for-touch","title":"PyTouch: A Machine Learning Library for Touch Processing","date":"2021-05-26","arxiv_id":"2105.12791","repositories_listed":1,"syntology":null},{"url":"/paper/project-codenet-a-large-scale-ai-for-code","slug":"project-codenet-a-large-scale-ai-for-code","title":"CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks","date":"2021-05-25","arxiv_id":"2105.12655","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/project-codenet-a-large-scale-ai-for-code#ran","syntology_url":"https://syntology.ai/paper/2105.12655","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.12655"}},"official":{"repos":["IBM/Project_CodeNet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/high-level-camera-lidar-fusion-for-3d-object","slug":"high-level-camera-lidar-fusion-for-3d-object","title":"High-level camera-LiDAR fusion for 3D object detection with machine learning","date":"2021-05-24","arxiv_id":"2105.11060","repositories_listed":1,"syntology":null},{"url":"/paper/vanilla-verbalized-answers-in-natural","slug":"vanilla-verbalized-answers-in-natural","title":"VANiLLa : Verbalized Answers in Natural Language at Large Scale","date":"2021-05-24","arxiv_id":"2105.11407","repositories_listed":1,"syntology":null},{"url":"/paper/zero-initialised-unsupervised-active-learning","slug":"zero-initialised-unsupervised-active-learning","title":"Zero Initialised Unsupervised Active Learning by Optimally Balanced Entropy-Based Sampling for Imbalanced Problems","date":"2021-05-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/rtfps-an-interactive-map-that-visualizes-and","slug":"rtfps-an-interactive-map-that-visualizes-and","title":"RtFPS: An Interactive Map that Visualizes and Predicts Wildfires in the US","date":"2021-05-23","arxiv_id":"2105.10880","repositories_listed":1,"syntology":null},{"url":"/paper/data-curation-and-quality-assurance-for","slug":"data-curation-and-quality-assurance-for","title":"Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion Detection","date":"2021-05-20","arxiv_id":"2105.10041","repositories_listed":1,"syntology":null},{"url":"/paper/conformal-histogram-regression","slug":"conformal-histogram-regression","title":"Conformal Prediction using Conditional Histograms","date":"2021-05-18","arxiv_id":"2105.08747","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/conformal-histogram-regression#ran","syntology_url":"https://syntology.ai/paper/2105.08747","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.08747"}},"official":{"repos":["msesia/chr"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/machine-learning-on-knowledge-graphs-for","slug":"machine-learning-on-knowledge-graphs-for","title":"Machine learning on knowledge graphs for context-aware security monitoring","date":"2021-05-18","arxiv_id":"2105.08741","repositories_listed":1,"syntology":null},{"url":"/paper/quantifying-sources-of-uncertainty-in-drug","slug":"quantifying-sources-of-uncertainty-in-drug","title":"Quantifying sources of uncertainty in drug discovery predictions with probabilistic models","date":"2021-05-18","arxiv_id":"2105.09474","repositories_listed":1,"syntology":null},{"url":"/paper/algorithm-agnostic-explainability-for","slug":"algorithm-agnostic-explainability-for","title":"Algorithm-Agnostic Explainability for Unsupervised Clustering","date":"2021-05-17","arxiv_id":"2105.08053","repositories_listed":1,"syntology":null},{"url":"/paper/open-set-recognition-based-on-the-combination","slug":"open-set-recognition-based-on-the-combination","title":"Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios","date":"2021-05-17","arxiv_id":"2105.07635","repositories_listed":1,"syntology":null},{"url":"/paper/power-grid-stability-prediction-using","slug":"power-grid-stability-prediction-using","title":"Power-grid stability predictions using transferable machine learning","date":"2021-05-17","arxiv_id":"2105.07562","repositories_listed":1,"syntology":null},{"url":"/paper/towards-demystifying-serverless-machine","slug":"towards-demystifying-serverless-machine","title":"Towards Demystifying Serverless Machine Learning Training","date":"2021-05-17","arxiv_id":"2105.07806","repositories_listed":1,"syntology":null},{"url":"/paper/are-convolutional-neural-networks-or","slug":"are-convolutional-neural-networks-or","title":"Are Convolutional Neural Networks or Transformers more like human vision?","date":"2021-05-15","arxiv_id":"2105.07197","repositories_listed":1,"syntology":null},{"url":"/paper/quantified-sleep-machine-learning-techniques","slug":"quantified-sleep-machine-learning-techniques","title":"Quantified Sleep: Machine learning techniques for observational n-of-1 studies","date":"2021-05-14","arxiv_id":"2105.06811","repositories_listed":1,"syntology":null},{"url":"/paper/addressing-fairness-bias-and-class-imbalance","slug":"addressing-fairness-bias-and-class-imbalance","title":"Addressing Fairness, Bias and Class Imbalance in Machine Learning: the FBI-loss","date":"2021-05-13","arxiv_id":"2105.06345","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-comparison-of-bias-reduction","slug":"an-empirical-comparison-of-bias-reduction","title":"An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings","date":"2021-05-13","arxiv_id":"2105.06442","repositories_listed":1,"syntology":null},{"url":"/paper/simnet-computer-architecture-simulation-using","slug":"simnet-computer-architecture-simulation-using","title":"SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning","date":"2021-05-12","arxiv_id":"2105.05821","repositories_listed":1,"syntology":null},{"url":"/paper/addressing-documentation-debt-in-machine","slug":"addressing-documentation-debt-in-machine","title":"Addressing \"Documentation Debt\" in Machine Learning Research: A Retrospective Datasheet for BookCorpus","date":"2021-05-11","arxiv_id":"2105.05241","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/addressing-documentation-debt-in-machine#ran","syntology_url":"https://syntology.ai/paper/2105.05241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.05241"}},"official":{"repos":["jackbandy/bookcorpus-datasheet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/prediction-of-soft-proton-intensities-in-the","slug":"prediction-of-soft-proton-intensities-in-the","title":"Prediction of soft proton intensities in the near-Earth space using machine learning","date":"2021-05-11","arxiv_id":"2105.15108","repositories_listed":1,"syntology":null},{"url":"/paper/optimising-resource-management-for-embedded","slug":"optimising-resource-management-for-embedded","title":"Optimising Resource Management for Embedded Machine Learning","date":"2021-05-08","arxiv_id":"2105.03608","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-machine-learning-for-high","slug":"interpretable-machine-learning-for-high","title":"Interpretable machine learning for high-dimensional trajectories of aging health","date":"2021-05-07","arxiv_id":"2105.03410","repositories_listed":1,"syntology":null},{"url":"/paper/structured-dataset-documentation-a-datasheet","slug":"structured-dataset-documentation-a-datasheet","title":"Structured dataset documentation: a datasheet for CheXpert","date":"2021-05-07","arxiv_id":"2105.03020","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-logistical-difficulties-and-findings","slug":"on-the-logistical-difficulties-and-findings","title":"On the logistical difficulties and findings of Jopara Sentiment Analysis","date":"2021-05-06","arxiv_id":"2105.02947","repositories_listed":1,"syntology":null},{"url":"/paper/point-cloud-audio-processing","slug":"point-cloud-audio-processing","title":"Point Cloud Audio Processing","date":"2021-05-06","arxiv_id":"2105.02469","repositories_listed":1,"syntology":null},{"url":"/paper/two4two-evaluating-interpretable-machine","slug":"two4two-evaluating-interpretable-machine","title":"Two4Two: Evaluating Interpretable Machine Learning - A Synthetic Dataset For Controlled Experiments","date":"2021-05-06","arxiv_id":"2105.02825","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/two4two-evaluating-interpretable-machine#ran","syntology_url":"https://syntology.ai/paper/2105.02825","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.02825"}},"official":{"repos":["mschuessler/two4two"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/attack-agnostic-adversarial-detection-on","slug":"attack-agnostic-adversarial-detection-on","title":"Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning","date":"2021-05-05","arxiv_id":"2105.01959","repositories_listed":1,"syntology":null},{"url":"/paper/learning-3d-granular-flow-simulations","slug":"learning-3d-granular-flow-simulations","title":"Learning 3D Granular Flow Simulations","date":"2021-05-04","arxiv_id":"2105.01636","repositories_listed":1,"syntology":null},{"url":"/paper/using-twitter-attribute-information-to","slug":"using-twitter-attribute-information-to","title":"Using Twitter Attribute Information to Predict Stock Prices","date":"2021-05-04","arxiv_id":"2105.01402","repositories_listed":1,"syntology":null},{"url":"/paper/the-tracking-machine-learning-challenge-1","slug":"the-tracking-machine-learning-challenge-1","title":"The Tracking Machine Learning challenge : Throughput phase","date":"2021-05-03","arxiv_id":"2105.01160","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-tracking-machine-learning-challenge-1#ran","syntology_url":"https://syntology.ai/paper/2105.01160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.01160"}},"official":{"repos":["LAL/trackml-library"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/emotion-recognition-of-the-singing-voice","slug":"emotion-recognition-of-the-singing-voice","title":"Emotion Recognition of the Singing Voice: Toward a Real-Time Analysis Tool for Singers","date":"2021-05-01","arxiv_id":"2105.00173","repositories_listed":1,"syntology":null},{"url":"/paper/flattening-multiparameter-hierarchical","slug":"flattening-multiparameter-hierarchical","title":"Flattening Multiparameter Hierarchical Clustering Functors","date":"2021-04-30","arxiv_id":"2104.14734","repositories_listed":1,"syntology":null},{"url":"/paper/forming-ensembles-at-runtime-a-machine","slug":"forming-ensembles-at-runtime-a-machine","title":"Forming Ensembles at Runtime: A Machine Learning Approach","date":"2021-04-30","arxiv_id":"2104.14848","repositories_listed":1,"syntology":null},{"url":"/paper/search-algorithms-for-automated-hyper","slug":"search-algorithms-for-automated-hyper","title":"Search Algorithms for Automated Hyper-Parameter Tuning","date":"2021-04-29","arxiv_id":"2104.14677","repositories_listed":1,"syntology":null},{"url":"/paper/you-can-still-achieve-fairness-without","slug":"you-can-still-achieve-fairness-without","title":"Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features","date":"2021-04-29","arxiv_id":"2104.14537","repositories_listed":1,"syntology":null},{"url":"/paper/algorithmic-factors-influencing-bias-in","slug":"algorithmic-factors-influencing-bias-in","title":"Algorithmic Factors Influencing Bias in Machine Learning","date":"2021-04-28","arxiv_id":"2104.14014","repositories_listed":1,"syntology":null},{"url":"/paper/deepsatdata-building-large-scale-datasets-of","slug":"deepsatdata-building-large-scale-datasets-of","title":"DeepSatData: Building large scale datasets of satellite images for training machine learning models","date":"2021-04-28","arxiv_id":"2104.13824","repositories_listed":1,"syntology":null},{"url":"/paper/a-human-centered-interpretability-framework","slug":"a-human-centered-interpretability-framework","title":"From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence","date":"2021-04-27","arxiv_id":"2104.13299","repositories_listed":1,"syntology":null},{"url":"/paper/invariant-polynomials-and-machine-learning","slug":"invariant-polynomials-and-machine-learning","title":"Invariant polynomials and machine learning","date":"2021-04-26","arxiv_id":"2104.12733","repositories_listed":1,"syntology":null},{"url":"/paper/large-scale-memory-failure-prediction-using","slug":"large-scale-memory-failure-prediction-using","title":"Highly Efficient Memory Failure Prediction using Mcelog-based Data Mining and Machine Learning","date":"2021-04-24","arxiv_id":"2105.04547","repositories_listed":1,"syntology":null},{"url":"/paper/grouped-feature-importance-and-combined","slug":"grouped-feature-importance-and-combined","title":"Grouped Feature Importance and Combined Features Effect Plot","date":"2021-04-23","arxiv_id":"2104.11688","repositories_listed":1,"syntology":null},{"url":"/paper/knodle-modular-weakly-supervised-learning","slug":"knodle-modular-weakly-supervised-learning","title":"Knodle: Modular Weakly Supervised Learning with PyTorch","date":"2021-04-23","arxiv_id":"2104.11557","repositories_listed":1,"syntology":null},{"url":"/paper/cryptgpu-fast-privacy-preserving-machine","slug":"cryptgpu-fast-privacy-preserving-machine","title":"CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU","date":"2021-04-22","arxiv_id":"2104.10949","repositories_listed":1,"syntology":null},{"url":"/paper/dataset-inference-ownership-resolution-in-1","slug":"dataset-inference-ownership-resolution-in-1","title":"Dataset Inference: Ownership Resolution in Machine Learning","date":"2021-04-21","arxiv_id":"2104.10706","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-optimization-is-superior-to-random","slug":"bayesian-optimization-is-superior-to-random","title":"Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020","date":"2021-04-20","arxiv_id":"2104.10201","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bayesian-optimization-is-superior-to-random#ran","syntology_url":"https://syntology.ai/paper/2104.10201","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.10201"}},"official":null}},{"url":"/paper/back-training-excels-self-training-at","slug":"back-training-excels-self-training-at","title":"Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval","date":"2021-04-18","arxiv_id":"2104.08801","repositories_listed":1,"syntology":null},{"url":"/paper/scale-adv-a-joint-attack-on-image-scaling-and","slug":"scale-adv-a-joint-attack-on-image-scaling-and","title":"Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems","date":"2021-04-18","arxiv_id":"2104.08690","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/scale-adv-a-joint-attack-on-image-scaling-and#ran","syntology_url":"https://syntology.ai/paper/2104.08690","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.08690"}},"official":{"repos":["wi-pi/rethinking-image-scaling-attacks"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}}],"record_sha256":"7502eb46711b2976d239561245a9ea6434708854c4fc5a32f95ac37ef47ad12f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}