{"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/deep-learning/papers/ran/2","list_of":"/task/deep-learning","task":"Deep 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":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":2,"pages_in_order":5,"rows_per_page":100,"rows":[101,200],"of":410,"counts":{"archive_papers_tagged":9423,"with_a_code_link":2693,"where_syntology_ran_a_sample":410,"not_listed_spam_title":0,"listed":9423,"listed_where_code_ran":410,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":356,"every_run_a_failure_of_syntologys_instrument":54,"listed_with_a_run_with_no_instrument_failure":356,"listed_every_run_a_failure_of_syntologys_instrument":54,"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/deep-learning/papers/ran/1","prev":"/task/deep-learning/papers/ran/1","next":"/task/deep-learning/papers/ran/3","papers":[{"url":"/paper/pac-bayes-compression-bounds-so-tight-that","slug":"pac-bayes-compression-bounds-so-tight-that","title":"PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization","date":"2022-11-24","arxiv_id":"2211.13609","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pac-bayes-compression-bounds-so-tight-that#ran","syntology_url":"https://syntology.ai/paper/2211.13609","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.13609"}},"official":{"repos":["activatedgeek/tight-pac-bayes"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/velo-training-versatile-learned-optimizers-by","slug":"velo-training-versatile-learned-optimizers-by","title":"VeLO: Training Versatile Learned Optimizers by Scaling Up","date":"2022-11-17","arxiv_id":"2211.09760","repositories_listed":2,"syntology":{"n":20,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/velo-training-versatile-learned-optimizers-by#ran","syntology_url":"https://syntology.ai/paper/2211.09760","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.09760"}},"official":{"repos":["google/learned_optimization"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/backdoor-attacks-for-remote-sensing-data-with","slug":"backdoor-attacks-for-remote-sensing-data-with","title":"Backdoor Attacks for Remote Sensing Data with Wavelet Transform","date":"2022-11-15","arxiv_id":"2211.08044","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"8 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/backdoor-attacks-for-remote-sensing-data-with#ran","syntology_url":"https://syntology.ai/paper/2211.08044","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.08044"}},"official":{"repos":["ndraeger/waba"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-for-time-series-anomaly","slug":"deep-learning-for-time-series-anomaly","title":"Deep Learning for Time Series Anomaly Detection: A Survey","date":"2022-11-09","arxiv_id":"2211.05244","repositories_listed":2,"syntology":{"n":15,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-learning-for-time-series-anomaly#ran","syntology_url":"https://syntology.ai/paper/2211.05244","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.05244"}},"official":{"repos":["zamanzadeh/ts-anomaly-benchmark"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/monai-an-open-source-framework-for-deep","slug":"monai-an-open-source-framework-for-deep","title":"MONAI: An open-source framework for deep learning in healthcare","date":"2022-11-04","arxiv_id":"2211.02701","repositories_listed":2,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"phrase":"9 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/monai-an-open-source-framework-for-deep#ran","syntology_url":"https://syntology.ai/paper/2211.02701","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.02701"}},"official":{"repos":["Project-MONAI/MONAI"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/interpretable-geometric-deep-learning-via","slug":"interpretable-geometric-deep-learning-via","title":"Interpretable Geometric Deep Learning via Learnable Randomness Injection","date":"2022-10-30","arxiv_id":"2210.16966","repositories_listed":2,"syntology":{"n":13,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"7 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; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/interpretable-geometric-deep-learning-via#ran","syntology_url":"https://syntology.ai/paper/2210.16966","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.16966"}},"official":{"repos":["graph-com/lri"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/accelerated-linearized-laplace-approximation","slug":"accelerated-linearized-laplace-approximation","title":"Accelerated Linearized Laplace Approximation for Bayesian Deep Learning","date":"2022-10-23","arxiv_id":"2210.12642","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":1,"n_ran_checked":2,"n_instrument":3,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":8,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/accelerated-linearized-laplace-approximation#ran","syntology_url":"https://syntology.ai/paper/2210.12642","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12642"}},"official":{"repos":["thudzj/ella"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-in-single-cell-analysis","slug":"deep-learning-in-single-cell-analysis","title":"Deep Learning in Single-Cell Analysis","date":"2022-10-22","arxiv_id":"2210.12385","repositories_listed":6,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-learning-in-single-cell-analysis#ran","syntology_url":"https://syntology.ai/paper/2210.12385","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12385"}},"official":{"repos":["omicsml/dance","cmzuo11/dcca","scverse/scvi-tools","vanvalenlab/deepcell-tf","wukevin/babel","zjufanlab/scdeepsort"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hidet-task-mapping-programming-paradigm-for","slug":"hidet-task-mapping-programming-paradigm-for","title":"Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs","date":"2022-10-18","arxiv_id":"2210.09603","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/hidet-task-mapping-programming-paradigm-for#ran","syntology_url":"https://syntology.ai/paper/2210.09603","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09603"}},"official":{"repos":["hidet-org/hidet","yaoyaoding/hidet-artifacts"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/self-explaining-deep-models-with-logic-rule","slug":"self-explaining-deep-models-with-logic-rule","title":"Self-explaining deep models with logic rule reasoning","date":"2022-10-13","arxiv_id":"2210.07024","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":2,"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 2 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; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/self-explaining-deep-models-with-logic-rule#ran","syntology_url":"https://syntology.ai/paper/2210.07024","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.07024"}},"official":{"repos":["archon159/selor"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-comprehensive-benchmark-for-covid-19","slug":"a-comprehensive-benchmark-for-covid-19","title":"A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care","date":"2022-09-16","arxiv_id":"2209.07805","repositories_listed":3,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"phrase":"7 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-comprehensive-benchmark-for-covid-19#ran","syntology_url":"https://syntology.ai/paper/2209.07805","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.07805"}},"official":{"repos":["yhzhu99/covid-ehr-benchmarks","yhzhu99/pyehr","yhzhu99/pyehr-playground"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/4denoisenet-adverse-weather-denoising-from","slug":"4denoisenet-adverse-weather-denoising-from","title":"4DenoiseNet: Adverse Weather Denoising from Adjacent Point Clouds","date":"2022-09-15","arxiv_id":"2209.07121","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":1,"n_no_contract":0,"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, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/4denoisenet-adverse-weather-denoising-from#ran","syntology_url":"https://syntology.ai/paper/2209.07121","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.07121"}},"official":{"repos":["alvariseppanen/4denoisenet"],"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/fp8-formats-for-deep-learning","slug":"fp8-formats-for-deep-learning","title":"FP8 Formats for Deep Learning","date":"2022-09-12","arxiv_id":"2209.05433","repositories_listed":4,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/fp8-formats-for-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2209.05433","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.05433"}},"official":null}},{"url":"/paper/cas4dl-christoffel-adaptive-sampling-for","slug":"cas4dl-christoffel-adaptive-sampling-for","title":"CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep Learning","date":"2022-08-25","arxiv_id":"2208.12190","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/cas4dl-christoffel-adaptive-sampling-for#ran","syntology_url":"https://syntology.ai/paper/2208.12190","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.12190"}},"official":{"repos":["jmcardenas/cas4dl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/adan-adaptive-nesterov-momentum-algorithm-for","slug":"adan-adaptive-nesterov-momentum-algorithm-for","title":"Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models","date":"2022-08-13","arxiv_id":"2208.06677","repositories_listed":9,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adan-adaptive-nesterov-momentum-algorithm-for#ran","syntology_url":"https://syntology.ai/paper/2208.06677","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.06677"}},"official":{"repos":["sail-sg/adan"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/generating-physically-consistent-high","slug":"generating-physically-consistent-high","title":"Hard-Constrained Deep Learning for Climate Downscaling","date":"2022-08-08","arxiv_id":"2208.05424","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/generating-physically-consistent-high#ran","syntology_url":"https://syntology.ai/paper/2208.05424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.05424"}},"official":{"repos":["rolnicklab/constrained-downscaling"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-understanding-mixture-of-experts-in","slug":"towards-understanding-mixture-of-experts-in","title":"Towards Understanding Mixture of Experts in Deep Learning","date":"2022-08-04","arxiv_id":"2208.02813","repositories_listed":2,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"5 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-understanding-mixture-of-experts-in#ran","syntology_url":"https://syntology.ai/paper/2208.02813","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.02813"}},"official":{"repos":["uclaml/MoE"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/human-trajectory-prediction-via-neural-social","slug":"human-trajectory-prediction-via-neural-social","title":"Human Trajectory Prediction via Neural Social Physics","date":"2022-07-21","arxiv_id":"2207.10435","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":0,"n_no_contract":1,"n_pointer_only":6,"phrase":"4 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/human-trajectory-prediction-via-neural-social#ran","syntology_url":"https://syntology.ai/paper/2207.10435","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10435"}},"official":{"repos":["realcrane/human-trajectory-prediction-via-neural-social-physics"],"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/image-super-resolution-with-deep-dictionary","slug":"image-super-resolution-with-deep-dictionary","title":"Image Super-Resolution with Deep Dictionary","date":"2022-07-19","arxiv_id":"2207.09228","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":7,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 7 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) · 1 unverified; every one of the 7 samples that ran constructed an object rather than computing a result","sample_list":"/paper/image-super-resolution-with-deep-dictionary#ran","syntology_url":"https://syntology.ai/paper/2207.09228","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.09228"}},"official":{"repos":["shuntama/srdd"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":7,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-and-its-applications-to-wifi","slug":"deep-learning-and-its-applications-to-wifi","title":"SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing","date":"2022-07-16","arxiv_id":"2207.07859","repositories_listed":2,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"10 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deep-learning-and-its-applications-to-wifi#ran","syntology_url":"https://syntology.ai/paper/2207.07859","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.07859"}},"official":{"repos":["chenxinyan-sg/wifi-csi-sensing-benchmark","xyanchen/wifi-csi-sensing-benchmark"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/bayescap-bayesian-identity-cap-for-calibrated","slug":"bayescap-bayesian-identity-cap-for-calibrated","title":"BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks","date":"2022-07-14","arxiv_id":"2207.06873","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 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) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/bayescap-bayesian-identity-cap-for-calibrated#ran","syntology_url":"https://syntology.ai/paper/2207.06873","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.06873"}},"official":{"repos":["explainableml/bayescap"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sparsetir-composable-abstractions-for-sparse","slug":"sparsetir-composable-abstractions-for-sparse","title":"SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning","date":"2022-07-11","arxiv_id":"2207.04606","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":2,"n_no_contract":2,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 2 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sparsetir-composable-abstractions-for-sparse#ran","syntology_url":"https://syntology.ai/paper/2207.04606","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.04606"}},"official":{"repos":["uwsampl/sparsetir","uwsampl/sparsetir-artifact"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/revisiting-pretraining-objectives-for-tabular","slug":"revisiting-pretraining-objectives-for-tabular","title":"Revisiting Pretraining Objectives for Tabular Deep Learning","date":"2022-07-07","arxiv_id":"2207.03208","repositories_listed":2,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"8 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; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/revisiting-pretraining-objectives-for-tabular#ran","syntology_url":"https://syntology.ai/paper/2207.03208","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.03208"}},"official":{"repos":["puhsu/tabular-dl-pretrain-objectives"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"url":"/paper/deep-learning-and-symbolic-regression-for","slug":"deep-learning-and-symbolic-regression-for","title":"Deep Learning and Symbolic Regression for Discovering Parametric Equations","date":"2022-07-01","arxiv_id":"2207.00529","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":3,"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/deep-learning-and-symbolic-regression-for#ran","syntology_url":"https://syntology.ai/paper/2207.00529","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.00529"}},"official":{"repos":["samuelkim314/parametric-eql"],"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/single-phase-deep-learning-in-cortico","slug":"single-phase-deep-learning-in-cortico","title":"Single-phase deep learning in cortico-cortical networks","date":"2022-06-23","arxiv_id":"2206.11769","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":3,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":6,"phrase":"5 ran (of which 3 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) · 1 unverified","sample_list":"/paper/single-phase-deep-learning-in-cortico#ran","syntology_url":"https://syntology.ai/paper/2206.11769","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.11769"}},"official":{"repos":["neuralml/burstccn"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/robust-sde-based-variational-formulations-for","slug":"robust-sde-based-variational-formulations-for","title":"Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning","date":"2022-06-21","arxiv_id":"2206.10588","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/robust-sde-based-variational-formulations-for#ran","syntology_url":"https://syntology.ai/paper/2206.10588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.10588"}},"official":{"repos":["juliusberner/robust_kolmogorov"],"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/low-precision-stochastic-gradient-langevin-1","slug":"low-precision-stochastic-gradient-langevin-1","title":"Low-Precision Stochastic Gradient Langevin Dynamics","date":"2022-06-20","arxiv_id":"2206.09909","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":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/low-precision-stochastic-gradient-langevin-1#ran","syntology_url":"https://syntology.ai/paper/2206.09909","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.09909"}},"official":{"repos":["ruqizhang/low-precision-sgld"],"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/rethinking-bayesian-deep-learning-methods-for-1","slug":"rethinking-bayesian-deep-learning-methods-for-1","title":"Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation","date":"2022-06-18","arxiv_id":"2206.09293","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 3 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; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/rethinking-bayesian-deep-learning-methods-for-1#ran","syntology_url":"https://syntology.ai/paper/2206.09293","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.09293"}},"official":{"repos":["jianf-wang/gbdl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/near-exact-recovery-for-tomographic-inverse","slug":"near-exact-recovery-for-tomographic-inverse","title":"Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning","date":"2022-06-14","arxiv_id":"2206.07050","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/near-exact-recovery-for-tomographic-inverse#ran","syntology_url":"https://syntology.ai/paper/2206.07050","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.07050"}},"official":{"repos":["jmaces/aapm-ct-challenge"],"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"]}}},{"url":"/paper/film-ensemble-probabilistic-deep-learning-via","slug":"film-ensemble-probabilistic-deep-learning-via","title":"FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation","date":"2022-05-31","arxiv_id":"2206.00050","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 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) · 4 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/film-ensemble-probabilistic-deep-learning-via#ran","syntology_url":"https://syntology.ai/paper/2206.00050","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.00050"}},"official":{"repos":["prs-eth/film-ensemble"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/generalizing-brain-decoding-across-subjects","slug":"generalizing-brain-decoding-across-subjects","title":"Group-level Brain Decoding with Deep Learning","date":"2022-05-27","arxiv_id":"2205.14102","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/generalizing-brain-decoding-across-subjects#ran","syntology_url":"https://syntology.ai/paper/2205.14102","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14102"}},"official":{"repos":["ricsinaruto/meg-group-decode"],"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/pre-train-your-loss-easy-bayesian-transfer","slug":"pre-train-your-loss-easy-bayesian-transfer","title":"Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors","date":"2022-05-20","arxiv_id":"2205.10279","repositories_listed":2,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 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; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/pre-train-your-loss-easy-bayesian-transfer#ran","syntology_url":"https://syntology.ai/paper/2205.10279","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.10279"}},"official":{"repos":["hsouri/bayesiantransferlearning"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/deep-learning-methods-for-proximal-inference","slug":"deep-learning-methods-for-proximal-inference","title":"Deep Learning Methods for Proximal Inference via Maximum Moment Restriction","date":"2022-05-19","arxiv_id":"2205.09824","repositories_listed":2,"syntology":{"n":10,"n_ran":10,"n_constructed":5,"n_ran_checked":5,"n_instrument":5,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"10 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-learning-methods-for-proximal-inference#ran","syntology_url":"https://syntology.ai/paper/2205.09824","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.09824"}},"official":{"repos":["beamlab-hsph/neural-moment-matching-regression"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/film-frequency-improved-legendre-memory-model","slug":"film-frequency-improved-legendre-memory-model","title":"FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting","date":"2022-05-18","arxiv_id":"2205.08897","repositories_listed":3,"syntology":{"n":18,"n_ran":17,"n_constructed":2,"n_ran_checked":7,"n_instrument":10,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":2,"phrase":"17 ran (of which 2 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 10 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/film-frequency-improved-legendre-memory-model#ran","syntology_url":"https://syntology.ai/paper/2205.08897","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.08897"}},"official":{"repos":["WenjieDu/PyPOTS"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","named_in_paper"]}}},{"url":"/paper/efficient-automated-deep-learning-for-time","slug":"efficient-automated-deep-learning-for-time","title":"Efficient Automated Deep Learning for Time Series Forecasting","date":"2022-05-11","arxiv_id":"2205.05511","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/efficient-automated-deep-learning-for-time#ran","syntology_url":"https://syntology.ai/paper/2205.05511","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.05511"}},"official":{"repos":["automl/Auto-PyTorch"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-simple-approach-to-improve-single-model","slug":"a-simple-approach-to-improve-single-model","title":"A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness","date":"2022-05-01","arxiv_id":"2205.00403","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-simple-approach-to-improve-single-model#ran","syntology_url":"https://syntology.ai/paper/2205.00403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.00403"}},"official":{"repos":["google/uncertainty-baselines"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/video-polyp-segmentation-a-deep-learning","slug":"video-polyp-segmentation-a-deep-learning","title":"Video Polyp Segmentation: A Deep Learning Perspective","date":"2022-03-27","arxiv_id":"2203.14291","repositories_listed":4,"syntology":{"n":8,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"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) · 7 unverified","sample_list":"/paper/video-polyp-segmentation-a-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2203.14291","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14291"}},"official":{"repos":["DengPingFan/PraNet","GewelsJI/PNS-Net","gewelsji/vps"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/representation-agnostic-shape-fields-1","slug":"representation-agnostic-shape-fields-1","title":"Representation-Agnostic Shape Fields","date":"2022-03-19","arxiv_id":"2203.10259","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"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 1 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; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/representation-agnostic-shape-fields-1#ran","syntology_url":"https://syntology.ai/paper/2203.10259","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.10259"}},"official":{"repos":["seanywang0408/rasf"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/half-inverse-gradients-for-physical-deep-1","slug":"half-inverse-gradients-for-physical-deep-1","title":"Half-Inverse Gradients for Physical Deep Learning","date":"2022-03-18","arxiv_id":"2203.10131","repositories_listed":2,"syntology":{"n":5,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/half-inverse-gradients-for-physical-deep-1#ran","syntology_url":"https://syntology.ai/paper/2203.10131","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.10131"}},"official":{"repos":["tum-pbs/half-inverse-gradients"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-without-shortcuts-shaping-the-1","slug":"deep-learning-without-shortcuts-shaping-the-1","title":"Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers","date":"2022-03-15","arxiv_id":"2203.08120","repositories_listed":1,"syntology":{"n":24,"n_ran":14,"n_constructed":0,"n_ran_checked":12,"n_instrument":2,"n_unverified":10,"n_honours":7,"n_violates":1,"n_no_contract":4,"n_pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 7 honoured, 1 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/deep-learning-without-shortcuts-shaping-the-1#ran","syntology_url":"https://syntology.ai/paper/2203.08120","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.08120"}},"official":{"repos":["deepmind/dks"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":7,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/a-novel-perspective-to-look-at-attention-bi","slug":"a-novel-perspective-to-look-at-attention-bi","title":"A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification","date":"2022-03-14","arxiv_id":"2203.07216","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"5 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/a-novel-perspective-to-look-at-attention-bi#ran","syntology_url":"https://syntology.ai/paper/2203.07216","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.07216"}},"official":{"repos":["ruixinhua/batm"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pd-flow-a-point-cloud-denoising-framework","slug":"pd-flow-a-point-cloud-denoising-framework","title":"PD-Flow: A Point Cloud Denoising Framework with Normalizing Flows","date":"2022-03-11","arxiv_id":"2203.05940","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/pd-flow-a-point-cloud-denoising-framework#ran","syntology_url":"https://syntology.ai/paper/2203.05940","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.05940"}},"official":{"repos":["unknownue/pdflow"],"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/on-embeddings-for-numerical-features-in","slug":"on-embeddings-for-numerical-features-in","title":"On Embeddings for Numerical Features in Tabular Deep Learning","date":"2022-03-10","arxiv_id":"2203.05556","repositories_listed":4,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/on-embeddings-for-numerical-features-in#ran","syntology_url":"https://syntology.ai/paper/2203.05556","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.05556"}},"official":{"repos":["Yura52/tabular-dl-num-embeddings","yandex-research/tabular-dl-num-embeddings"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/chemicalx-a-deep-learning-library-for-drug","slug":"chemicalx-a-deep-learning-library-for-drug","title":"ChemicalX: A Deep Learning Library for Drug Pair Scoring","date":"2022-02-10","arxiv_id":"2202.05240","repositories_listed":2,"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/chemicalx-a-deep-learning-library-for-drug#ran","syntology_url":"https://syntology.ai/paper/2202.05240","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.05240"}},"official":{"repos":["AstraZeneca/chemicalx"],"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/penalizing-gradient-norm-for-efficiently","slug":"penalizing-gradient-norm-for-efficiently","title":"Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning","date":"2022-02-08","arxiv_id":"2202.03599","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/penalizing-gradient-norm-for-efficiently#ran","syntology_url":"https://syntology.ai/paper/2202.03599","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.03599"}},"official":{"repos":["zhaoyang-0204/gnp"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/from-data-to-functa-your-data-point-is-a","slug":"from-data-to-functa-your-data-point-is-a","title":"From data to functa: Your data point is a function and you can treat it like one","date":"2022-01-28","arxiv_id":"2201.12204","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/from-data-to-functa-your-data-point-is-a#ran","syntology_url":"https://syntology.ai/paper/2201.12204","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.12204"}},"official":{"repos":["deepmind/functa"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/bounding-training-data-reconstruction-in","slug":"bounding-training-data-reconstruction-in","title":"Bounding Training Data Reconstruction in Private (Deep) Learning","date":"2022-01-28","arxiv_id":"2201.12383","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bounding-training-data-reconstruction-in#ran","syntology_url":"https://syntology.ai/paper/2201.12383","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.12383"}},"official":{"repos":["facebookresearch/bounding_data_reconstruction"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ensemble-learning-priors-unfolding-for","slug":"ensemble-learning-priors-unfolding-for","title":"Ensemble learning priors unfolding for scalable Snapshot Compressive Sensing","date":"2022-01-25","arxiv_id":"2201.10419","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":6,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":7,"phrase":"6 ran (of which 6 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) · 1 unverified; every one of the 6 samples that ran constructed an object rather than computing a result","sample_list":"/paper/ensemble-learning-priors-unfolding-for#ran","syntology_url":"https://syntology.ai/paper/2201.10419","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.10419"}},"official":{"repos":["integritynoble/ELP-Unfolding"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/3d-segmentation-with-fully-trainable-gabor","slug":"3d-segmentation-with-fully-trainable-gabor","title":"3D Segmentation with Fully Trainable Gabor Kernels and Pearson's Correlation Coefficient","date":"2022-01-10","arxiv_id":"2201.03644","repositories_listed":1,"syntology":{"n":20,"n_ran":18,"n_constructed":0,"n_ran_checked":18,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":18,"n_pointer_only":0,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 0 violated, 18 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/3d-segmentation-with-fully-trainable-gabor#ran","syntology_url":"https://syntology.ai/paper/2201.03644","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.03644"}},"official":null}},{"url":"/paper/quantifying-uncertainty-in-deep-learning","slug":"quantifying-uncertainty-in-deep-learning","title":"Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy Classification","date":"2022-01-04","arxiv_id":"2201.01203","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/quantifying-uncertainty-in-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2201.01203","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.01203"}},"official":{"repos":["devinamhn/radiogalaxies-bbb"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/automated-deep-learning-neural-architecture","slug":"automated-deep-learning-neural-architecture","title":"Automated Deep Learning: Neural Architecture Search Is Not the End","date":"2021-12-16","arxiv_id":"2112.09245","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/automated-deep-learning-neural-architecture#ran","syntology_url":"https://syntology.ai/paper/2112.09245","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.09245"}},"official":{"repos":["D-X-Y/Awesome-AutoDL"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/steerable-discovery-of-neural-audio-effects","slug":"steerable-discovery-of-neural-audio-effects","title":"Steerable discovery of neural audio effects","date":"2021-12-06","arxiv_id":"2112.02926","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/steerable-discovery-of-neural-audio-effects#ran","syntology_url":"https://syntology.ai/paper/2112.02926","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.02926"}},"official":{"repos":["csteinmetz1/steerable-nafx"],"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/pytorchvideo-a-deep-learning-library-for","slug":"pytorchvideo-a-deep-learning-library-for","title":"PyTorchVideo: A Deep Learning Library for Video Understanding","date":"2021-11-18","arxiv_id":"2111.09887","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":0,"n_no_contract":5,"n_pointer_only":7,"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) · 2 unverified","sample_list":"/paper/pytorchvideo-a-deep-learning-library-for#ran","syntology_url":"https://syntology.ai/paper/2111.09887","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.09887"}},"official":{"repos":["facebookresearch/pytorchvideo"],"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/generating-3d-molecules-conditional-on","slug":"generating-3d-molecules-conditional-on","title":"Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models","date":"2021-10-28","arxiv_id":"2110.15200","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/generating-3d-molecules-conditional-on#ran","syntology_url":"https://syntology.ai/paper/2110.15200","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.15200"}},"official":{"repos":["mattragoza/liGAN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/neuro-symbolic-forward-reasoning-1","slug":"neuro-symbolic-forward-reasoning-1","title":"Neuro-Symbolic Forward Reasoning","date":"2021-10-18","arxiv_id":"2110.09383","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/neuro-symbolic-forward-reasoning-1#ran","syntology_url":"https://syntology.ai/paper/2110.09383","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.09383"}},"official":{"repos":["ml-research/nsfr"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/predictive-models-of-rna-degradation-through","slug":"predictive-models-of-rna-degradation-through","title":"Deep learning models for predicting RNA degradation via dual crowdsourcing","date":"2021-10-14","arxiv_id":"2110.07531","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"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; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/predictive-models-of-rna-degradation-through#ran","syntology_url":"https://syntology.ai/paper/2110.07531","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.07531"}},"official":{"repos":["eternagame/KaggleOpenVaccine"],"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/imitating-deep-learning-dynamics-via-locally","slug":"imitating-deep-learning-dynamics-via-locally","title":"Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations","date":"2021-10-11","arxiv_id":"2110.05960","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"6 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; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/imitating-deep-learning-dynamics-via-locally#ran","syntology_url":"https://syntology.ai/paper/2110.05960","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.05960"}},"official":{"repos":["zjiayao/le_sde"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/relaysum-for-decentralized-deep-learning-on","slug":"relaysum-for-decentralized-deep-learning-on","title":"RelaySum for Decentralized Deep Learning on Heterogeneous Data","date":"2021-10-08","arxiv_id":"2110.04175","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"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: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/relaysum-for-decentralized-deep-learning-on#ran","syntology_url":"https://syntology.ai/paper/2110.04175","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.04175"}},"official":{"repos":["epfml/relaysgd"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-neural-networks-and-tabular-data-a","slug":"deep-neural-networks-and-tabular-data-a","title":"Deep Neural Networks and Tabular Data: A Survey","date":"2021-10-05","arxiv_id":"2110.01889","repositories_listed":2,"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/deep-neural-networks-and-tabular-data-a#ran","syntology_url":"https://syntology.ai/paper/2110.01889","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.01889"}},"official":{"repos":["kathrinse/tabsurvey"],"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/geometric-algebra-attention-networks-for","slug":"geometric-algebra-attention-networks-for","title":"Geometric Algebra Attention Networks for Small Point Clouds","date":"2021-10-05","arxiv_id":"2110.02393","repositories_listed":2,"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/geometric-algebra-attention-networks-for#ran","syntology_url":"https://syntology.ai/paper/2110.02393","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.02393"}},"official":{"repos":["klarh/flowws-keras-geometry"],"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/sparse-deep-learning-a-new-framework-immune","slug":"sparse-deep-learning-a-new-framework-immune","title":"Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration","date":"2021-10-01","arxiv_id":"2110.00653","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":2,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sparse-deep-learning-a-new-framework-immune#ran","syntology_url":"https://syntology.ai/paper/2110.00653","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.00653"}},"official":{"repos":["sylydya/sparse-deep-learning-a-new-framework-immuneto-local-traps-and-miscalibration"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":2,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/training-spiking-neural-networks-using-1","slug":"training-spiking-neural-networks-using-1","title":"Training Spiking Neural Networks Using Lessons From Deep Learning","date":"2021-09-27","arxiv_id":"2109.12894","repositories_listed":3,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/training-spiking-neural-networks-using-1#ran","syntology_url":"https://syntology.ai/paper/2109.12894","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.12894"}},"official":{"repos":["jeshraghian/snntorch"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/physics-based-deep-learning","slug":"physics-based-deep-learning","title":"Physics-based Deep Learning","date":"2021-09-11","arxiv_id":"2109.05237","repositories_listed":6,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/physics-based-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2109.05237","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.05237"}},"official":{"repos":["thunil/Physics-Based-Deep-Learning","tum-pbs/PhiFlow","tum-pbs/diffusion-based-flow-prediction","tum-pbs/pbdl-dataset","tum-pbs/pbdl-book"],"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":["named_in_paper","official"]}}},{"url":"/paper/dl-traff-survey-and-benchmark-of-deep","slug":"dl-traff-survey-and-benchmark-of-deep","title":"DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction","date":"2021-08-20","arxiv_id":"2108.09091","repositories_listed":3,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/dl-traff-survey-and-benchmark-of-deep#ran","syntology_url":"https://syntology.ai/paper/2108.09091","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.09091"}},"official":{"repos":["deepkashiwa20/dl-traff-graph","deepkashiwa20/dl-traff-grid"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-enhanced-dynamic-mode","slug":"deep-learning-enhanced-dynamic-mode","title":"Deep Learning Enhanced Dynamic Mode Decomposition","date":"2021-08-10","arxiv_id":"2108.04433","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/deep-learning-enhanced-dynamic-mode#ran","syntology_url":"https://syntology.ai/paper/2108.04433","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.04433"}},"official":{"repos":["jaylago/dldmd"],"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/heteroscedastic-temporal-variational","slug":"heteroscedastic-temporal-variational","title":"Heteroscedastic Temporal Variational Autoencoder For Irregular Time Series","date":"2021-07-23","arxiv_id":"2107.11350","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":3,"n_ran_checked":4,"n_instrument":3,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"7 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/heteroscedastic-temporal-variational#ran","syntology_url":"https://syntology.ai/paper/2107.11350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.11350"}},"official":{"repos":["reml-lab/hetvae"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/evidential-deep-learning-for-open-set-action","slug":"evidential-deep-learning-for-open-set-action","title":"Evidential Deep Learning for Open Set Action Recognition","date":"2021-07-21","arxiv_id":"2107.10161","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":3,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"5 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/evidential-deep-learning-for-open-set-action#ran","syntology_url":"https://syntology.ai/paper/2107.10161","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.10161"}},"official":{"repos":["Cogito2012/DEAR"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/recurrent-parameter-generators","slug":"recurrent-parameter-generators","title":"Compact and Optimal Deep Learning with Recurrent Parameter Generators","date":"2021-07-15","arxiv_id":"2107.07110","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/recurrent-parameter-generators#ran","syntology_url":"https://syntology.ai/paper/2107.07110","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.07110"}},"official":{"repos":["samaonline/Recurrent-Parameter-Generators"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/laplace-redux-effortless-bayesian-deep","slug":"laplace-redux-effortless-bayesian-deep","title":"Laplace Redux -- Effortless Bayesian Deep Learning","date":"2021-06-28","arxiv_id":"2106.14806","repositories_listed":6,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/laplace-redux-effortless-bayesian-deep#ran","syntology_url":"https://syntology.ai/paper/2106.14806","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.14806"}},"official":{"repos":["AlexImmer/Laplace","runame/laplace-redux"],"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/deep-learning-for-face-anti-spoofing-a-survey","slug":"deep-learning-for-face-anti-spoofing-a-survey","title":"Deep Learning for Face Anti-Spoofing: A Survey","date":"2021-06-28","arxiv_id":"2106.14948","repositories_listed":2,"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/deep-learning-for-face-anti-spoofing-a-survey#ran","syntology_url":"https://syntology.ai/paper/2106.14948","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.14948"}},"official":{"repos":["ZitongYu/DeepFAS"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/vindr-spinexr-a-deep-learning-framework-for","slug":"vindr-spinexr-a-deep-learning-framework-for","title":"VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs","date":"2021-06-24","arxiv_id":"2106.12930","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/vindr-spinexr-a-deep-learning-framework-for#ran","syntology_url":"https://syntology.ai/paper/2106.12930","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.12930"}},"official":{"repos":["vinbigdata-medical/vindr-spinexr"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/revisiting-deep-learning-models-for-tabular","slug":"revisiting-deep-learning-models-for-tabular","title":"Revisiting Deep Learning Models for Tabular Data","date":"2021-06-22","arxiv_id":"2106.11959","repositories_listed":11,"syntology":{"n":22,"n_ran":18,"n_constructed":0,"n_ran_checked":15,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":14,"n_pointer_only":1,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 1 violated, 14 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/revisiting-deep-learning-models-for-tabular#ran","syntology_url":"https://syntology.ai/paper/2106.11959","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.11959"}},"official":{"repos":["yandex-research/tabular-dl-revisiting-models","Yura52/tabular-dl-revisiting-models"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/deep-learning-for-functional-data-analysis","slug":"deep-learning-for-functional-data-analysis","title":"Deep Learning for Functional Data Analysis with Adaptive Basis Layers","date":"2021-06-19","arxiv_id":"2106.10414","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 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; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/deep-learning-for-functional-data-analysis#ran","syntology_url":"https://syntology.ai/paper/2106.10414","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.10414"}},"official":{"repos":["jwyyy/AdaFNN"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/steerable-partial-differential-operators-for","slug":"steerable-partial-differential-operators-for","title":"Steerable Partial Differential Operators for Equivariant Neural Networks","date":"2021-06-18","arxiv_id":"2106.10163","repositories_listed":4,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 0 unverified","sample_list":"/paper/steerable-partial-differential-operators-for#ran","syntology_url":"https://syntology.ai/paper/2106.10163","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.10163"}},"official":{"repos":["ejnnr/steerable_pdo_experiments","ejnnr/steerable_pdos"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/beyond-batchnorm-towards-a-general","slug":"beyond-batchnorm-towards-a-general","title":"Beyond BatchNorm: Towards a Unified Understanding of Normalization in Deep Learning","date":"2021-06-10","arxiv_id":"2106.05956","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 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; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/beyond-batchnorm-towards-a-general#ran","syntology_url":"https://syntology.ai/paper/2106.05956","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.05956"}},"official":{"repos":["EkdeepSLubana/BeyondBatchNorm"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-statistical-arbitrage","slug":"deep-learning-statistical-arbitrage","title":"Deep Learning Statistical Arbitrage","date":"2021-06-08","arxiv_id":"2106.04028","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-learning-statistical-arbitrage#ran","syntology_url":"https://syntology.ai/paper/2106.04028","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04028"}},"official":null}},{"url":"/paper/uncertainty-baselines-benchmarks-for","slug":"uncertainty-baselines-benchmarks-for","title":"Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning","date":"2021-06-07","arxiv_id":"2106.04015","repositories_listed":3,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/uncertainty-baselines-benchmarks-for#ran","syntology_url":"https://syntology.ai/paper/2106.04015","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04015"}},"official":{"repos":["google/uncertainty-baselines"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/preferencenet-encoding-human-preferences-in","slug":"preferencenet-encoding-human-preferences-in","title":"PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning","date":"2021-06-06","arxiv_id":"2106.03215","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/preferencenet-encoding-human-preferences-in#ran","syntology_url":"https://syntology.ai/paper/2106.03215","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03215"}},"official":{"repos":["neeharperi/PreferenceNet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tensor-normal-training-for-deep-learning","slug":"tensor-normal-training-for-deep-learning","title":"Tensor Normal Training for Deep Learning Models","date":"2021-06-05","arxiv_id":"2106.02925","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":3,"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/tensor-normal-training-for-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2106.02925","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.02925"}},"official":{"repos":["renyiryry/tnt_neurips_2021"],"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/k-mixup-regularization-for-deep-learning-via","slug":"k-mixup-regularization-for-deep-learning-via","title":"k-Mixup Regularization for Deep Learning via Optimal Transport","date":"2021-06-05","arxiv_id":"2106.02933","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/k-mixup-regularization-for-deep-learning-via#ran","syntology_url":"https://syntology.ai/paper/2106.02933","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.02933"}},"official":{"repos":["anminggu/kmixup-cifar10"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/self-attention-between-datapoints-going","slug":"self-attention-between-datapoints-going","title":"Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning","date":"2021-06-04","arxiv_id":"2106.02584","repositories_listed":3,"syntology":{"n":30,"n_ran":22,"n_constructed":4,"n_ran_checked":22,"n_instrument":0,"n_unverified":8,"n_honours":2,"n_violates":1,"n_no_contract":19,"n_pointer_only":0,"phrase":"22 ran (of which 4 constructed an object rather than computing a result; 22 with no instrument failure: 2 honoured, 1 violated, 19 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/self-attention-between-datapoints-going#ran","syntology_url":"https://syntology.ai/paper/2106.02584","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.02584"}},"official":{"repos":["OATML/Non-Parametric-Transformers"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":4,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/node-gam-neural-generalized-additive-model","slug":"node-gam-neural-generalized-additive-model","title":"NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning","date":"2021-06-03","arxiv_id":"2106.01613","repositories_listed":2,"syntology":{"n":15,"n_ran":9,"n_constructed":5,"n_ran_checked":8,"n_instrument":1,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"phrase":"9 ran (of which 5 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/node-gam-neural-generalized-additive-model#ran","syntology_url":"https://syntology.ai/paper/2106.01613","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.01613"}},"official":{"repos":["zzzace2000/nodegam"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/saint-improved-neural-networks-for-tabular","slug":"saint-improved-neural-networks-for-tabular","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","date":"2021-06-02","arxiv_id":"2106.01342","repositories_listed":7,"syntology":{"n":26,"n_ran":19,"n_constructed":0,"n_ran_checked":19,"n_instrument":0,"n_unverified":7,"n_honours":1,"n_violates":2,"n_no_contract":16,"n_pointer_only":13,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 1 honoured, 2 violated, 16 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/saint-improved-neural-networks-for-tabular#ran","syntology_url":"https://syntology.ai/paper/2106.01342","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.01342"}},"official":{"repos":["somepago/saint"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/locally-valid-and-discriminative-confidence","slug":"locally-valid-and-discriminative-confidence","title":"Locally Valid and Discriminative Prediction Intervals for Deep Learning Models","date":"2021-06-01","arxiv_id":"2106.00225","repositories_listed":1,"syntology":{"n":16,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/locally-valid-and-discriminative-confidence#ran","syntology_url":"https://syntology.ai/paper/2106.00225","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.00225"}},"official":{"repos":["zlin7/lvd"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/sparse-uncertainty-representation-in-deep-1","slug":"sparse-uncertainty-representation-in-deep-1","title":"Sparse Uncertainty Representation in Deep Learning with Inducing Weights","date":"2021-05-30","arxiv_id":"2105.14594","repositories_listed":0,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/sparse-uncertainty-representation-in-deep-1#ran","syntology_url":"https://syntology.ai/paper/2105.14594","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.14594"}},"official":null}},{"url":"/paper/neurallog-natural-language-inference-with","slug":"neurallog-natural-language-inference-with","title":"NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning","date":"2021-05-29","arxiv_id":"2105.14167","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/neurallog-natural-language-inference-with#ran","syntology_url":"https://syntology.ai/paper/2105.14167","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.14167"}},"official":{"repos":["eric11eca/NeuralLog"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/polygonal-unadjusted-langevin-algorithms","slug":"polygonal-unadjusted-langevin-algorithms","title":"Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks","date":"2021-05-28","arxiv_id":"2105.13937","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":4,"phrase":"9 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/polygonal-unadjusted-langevin-algorithms#ran","syntology_url":"https://syntology.ai/paper/2105.13937","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.13937"}},"official":null}},{"url":"/paper/xomivae-an-interpretable-deep-learning-model","slug":"xomivae-an-interpretable-deep-learning-model","title":"XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data","date":"2021-05-26","arxiv_id":"2105.12807","repositories_listed":2,"syntology":{"n":15,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":8,"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) · 8 unverified","sample_list":"/paper/xomivae-an-interpretable-deep-learning-model#ran","syntology_url":"https://syntology.ai/paper/2105.12807","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.12807"}},"official":{"repos":["zhangxiaoyu11/XOmiVAE"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-based-damage-mapping-with-insar","slug":"deep-learning-based-damage-mapping-with-insar","title":"Deep Learning-based Damage Mapping with InSAR Coherence Time Series","date":"2021-05-24","arxiv_id":"2105.11544","repositories_listed":2,"syntology":{"n":15,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/deep-learning-based-damage-mapping-with-insar#ran","syntology_url":"https://syntology.ai/paper/2105.11544","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.11544"}},"official":{"repos":["olliestephenson/dpm-rnn-public"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/deep-learning-hamiltonian-monte-carlo","slug":"deep-learning-hamiltonian-monte-carlo","title":"Deep Learning Hamiltonian Monte Carlo","date":"2021-05-07","arxiv_id":"2105.03418","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/deep-learning-hamiltonian-monte-carlo#ran","syntology_url":"https://syntology.ai/paper/2105.03418","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.03418"}},"official":{"repos":["saforem2/l2hmc-qcd"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/deepsmote-fusing-deep-learning-and-smote-for","slug":"deepsmote-fusing-deep-learning-and-smote-for","title":"DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data","date":"2021-05-05","arxiv_id":"2105.02340","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"2 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deepsmote-fusing-deep-learning-and-smote-for#ran","syntology_url":"https://syntology.ai/paper/2105.02340","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.02340"}},"official":{"repos":["dd1github/DeepSMOTE"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-with-self-supervision-and","slug":"deep-learning-with-self-supervision-and","title":"Deep learning with self-supervision and uncertainty regularization to count fish in underwater images","date":"2021-04-30","arxiv_id":"2104.14964","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":0,"n_no_contract":5,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/deep-learning-with-self-supervision-and#ran","syntology_url":"https://syntology.ai/paper/2104.14964","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.14964"}},"official":{"repos":["ptarling/DeepLearningFishCounting"],"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/faster-meta-update-strategy-for-noise-robust","slug":"faster-meta-update-strategy-for-noise-robust","title":"Faster Meta Update Strategy for Noise-Robust Deep Learning","date":"2021-04-30","arxiv_id":"2104.15092","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":1,"n_ran_checked":2,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":7,"phrase":"3 ran (of which 1 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) · 4 unverified","sample_list":"/paper/faster-meta-update-strategy-for-noise-robust#ran","syntology_url":"https://syntology.ai/paper/2104.15092","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.15092"}},"official":{"repos":["youjiangxu/FaMUS"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/pocketnet-a-smaller-neural-network-for-3d","slug":"pocketnet-a-smaller-neural-network-for-3d","title":"PocketNet: A Smaller Neural Network for Medical Image Analysis","date":"2021-04-21","arxiv_id":"2104.10745","repositories_listed":2,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/pocketnet-a-smaller-neural-network-for-3d#ran","syntology_url":"https://syntology.ai/paper/2104.10745","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.10745"}},"official":{"repos":["aecelaya/MIST"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/the-intrinsic-dimension-of-images-and-its-1","slug":"the-intrinsic-dimension-of-images-and-its-1","title":"The Intrinsic Dimension of Images and Its Impact on Learning","date":"2021-04-18","arxiv_id":"2104.08894","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":2,"n_instrument":5,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/the-intrinsic-dimension-of-images-and-its-1#ran","syntology_url":"https://syntology.ai/paper/2104.08894","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.08894"}},"official":{"repos":["ppope/dimensions"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/pytorch-geometric-temporal-spatiotemporal","slug":"pytorch-geometric-temporal-spatiotemporal","title":"PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models","date":"2021-04-15","arxiv_id":"2104.07788","repositories_listed":5,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/pytorch-geometric-temporal-spatiotemporal#ran","syntology_url":"https://syntology.ai/paper/2104.07788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.07788"}},"official":{"repos":["benedekrozemberczki/pytorch_geometric_temporal"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/an-investigation-of-critical-issues-in-bias","slug":"an-investigation-of-critical-issues-in-bias","title":"Are Bias Mitigation Techniques for Deep Learning Effective?","date":"2021-04-01","arxiv_id":"2104.00170","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"6 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; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/an-investigation-of-critical-issues-in-bias#ran","syntology_url":"https://syntology.ai/paper/2104.00170","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.00170"}},"official":{"repos":["erobic/bias-mitigators"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/an-experimental-review-on-deep-learning","slug":"an-experimental-review-on-deep-learning","title":"An Experimental Review on Deep Learning Architectures for Time Series Forecasting","date":"2021-03-22","arxiv_id":"2103.12057","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/an-experimental-review-on-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2103.12057","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12057"}},"official":{"repos":["pedrolarben/TimeSeriesForecasting-DeepLearning"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/estimating-and-improving-fairness-with","slug":"estimating-and-improving-fairness-with","title":"Estimating and Improving Fairness with Adversarial Learning","date":"2021-03-07","arxiv_id":"2103.04243","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/estimating-and-improving-fairness-with#ran","syntology_url":"https://syntology.ai/paper/2103.04243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.04243"}},"official":null}},{"url":"/paper/practical-and-private-deep-learning-without","slug":"practical-and-private-deep-learning-without","title":"Practical and Private (Deep) Learning without Sampling or Shuffling","date":"2021-02-26","arxiv_id":"2103.00039","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/practical-and-private-deep-learning-without#ran","syntology_url":"https://syntology.ai/paper/2103.00039","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.00039"}},"official":{"repos":["google-research/DP-FTRL","google-research/federated"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"6af7e9369005dbfe79a26b766459707b96f18c39a9c6f30b771ee2a988c444ff","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}