{"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/learning-theory/papers/ran/1","list_of":"/task/learning-theory","task":"Learning Theory","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":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,44],"of":44,"counts":{"archive_papers_tagged":852,"with_a_code_link":140,"where_syntology_ran_a_sample":44,"not_listed_spam_title":0,"listed":852,"listed_where_code_ran":44,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":37,"every_run_a_failure_of_syntologys_instrument":7,"listed_with_a_run_with_no_instrument_failure":37,"listed_every_run_a_failure_of_syntologys_instrument":7,"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/learning-theory/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/self-supervised-evolution-operator-learning","slug":"self-supervised-evolution-operator-learning","title":"Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems","date":"2025-05-24","arxiv_id":"2505.18671","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/self-supervised-evolution-operator-learning#ran","syntology_url":"https://syntology.ai/paper/2505.18671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.18671"}},"official":{"repos":["pietronvll/encoderops"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/gradient-equilibrium-in-online-learning","slug":"gradient-equilibrium-in-online-learning","title":"Gradient Equilibrium in Online Learning: Theory and Applications","date":"2025-01-14","arxiv_id":"2501.08330","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/gradient-equilibrium-in-online-learning#ran","syntology_url":"https://syntology.ai/paper/2501.08330","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.08330"}},"official":{"repos":["aangelopoulos/gradient-equilibrium"],"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/nlprompt-noise-label-prompt-learning-for","slug":"nlprompt-noise-label-prompt-learning-for","title":"NLPrompt: Noise-Label Prompt Learning for Vision-Language Models","date":"2024-12-02","arxiv_id":"2412.01256","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":9,"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) · 2 unverified","sample_list":"/paper/nlprompt-noise-label-prompt-learning-for#ran","syntology_url":"https://syntology.ai/paper/2412.01256","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.01256"}},"official":{"repos":["qunovo/NLPrompt"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-learning-from-vision-language","slug":"federated-learning-from-vision-language","title":"Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method","date":"2024-09-29","arxiv_id":"2409.19610","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":10,"phrase":"7 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; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/federated-learning-from-vision-language#ran","syntology_url":"https://syntology.ai/paper/2409.19610","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.19610"}},"official":{"repos":["PanBikang/PromptFolio"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/scaling-laws-for-the-value-of-individual-data","slug":"scaling-laws-for-the-value-of-individual-data","title":"Scaling Laws for the Value of Individual Data Points in Machine Learning","date":"2024-05-30","arxiv_id":"2405.20456","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/scaling-laws-for-the-value-of-individual-data#ran","syntology_url":"https://syntology.ai/paper/2405.20456","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.20456"}},"official":{"repos":["iancovert/data-scaling"],"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/unveiling-the-cycloid-trajectory-of-em","slug":"unveiling-the-cycloid-trajectory-of-em","title":"Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression","date":"2024-05-28","arxiv_id":"2405.18237","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"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; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unveiling-the-cycloid-trajectory-of-em#ran","syntology_url":"https://syntology.ai/paper/2405.18237","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.18237"}},"official":{"repos":["dassein/cycloid_em_mlr"],"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/understanding-llms-requires-more-than","slug":"understanding-llms-requires-more-than","title":"Position: Understanding LLMs Requires More Than Statistical Generalization","date":"2024-05-03","arxiv_id":"2405.01964","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/understanding-llms-requires-more-than#ran","syntology_url":"https://syntology.ai/paper/2405.01964","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.01964"}},"official":{"repos":["rpatrik96/llm-non-identifiability"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/data-driven-performance-guarantees-for","slug":"data-driven-performance-guarantees-for","title":"Data-Driven Performance Guarantees for Classical and Learned Optimizers","date":"2024-04-22","arxiv_id":"2404.13831","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":2,"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/data-driven-performance-guarantees-for#ran","syntology_url":"https://syntology.ai/paper/2404.13831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.13831"}},"official":{"repos":["stellatogrp/datadrivenoptimizerguarantees"],"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/active-test-time-adaptation-theoretical","slug":"active-test-time-adaptation-theoretical","title":"Active Test-Time Adaptation: Theoretical Analyses and An Algorithm","date":"2024-04-07","arxiv_id":"2404.05094","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/active-test-time-adaptation-theoretical#ran","syntology_url":"https://syntology.ai/paper/2404.05094","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.05094"}},"official":{"repos":["divelab/atta"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/causal-stonet-causal-inference-for-high","slug":"causal-stonet-causal-inference-for-high","title":"Causal-StoNet: Causal Inference for High-Dimensional Complex Data","date":"2024-03-27","arxiv_id":"2403.18994","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":4,"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/causal-stonet-causal-inference-for-high#ran","syntology_url":"https://syntology.ai/paper/2403.18994","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.18994"}},"official":{"repos":["nixay/causal-stonet"],"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/information-based-transductive-active","slug":"information-based-transductive-active","title":"Transductive Active Learning: Theory and Applications","date":"2024-02-13","arxiv_id":"2402.15898","repositories_listed":2,"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/information-based-transductive-active#ran","syntology_url":"https://syntology.ai/paper/2402.15898","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.15898"}},"official":null}},{"url":"/paper/stochastic-gradient-flow-dynamics-of-test","slug":"stochastic-gradient-flow-dynamics-of-test","title":"Stochastic Gradient Flow Dynamics of Test Risk and its Exact Solution for Weak Features","date":"2024-02-12","arxiv_id":"2402.07626","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/stochastic-gradient-flow-dynamics-of-test#ran","syntology_url":"https://syntology.ai/paper/2402.07626","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.07626"}},"official":{"repos":["rodsveiga/sgf_dyn"],"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/harmonics-of-learning-universal-fourier","slug":"harmonics-of-learning-universal-fourier","title":"Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks","date":"2023-12-13","arxiv_id":"2312.08550","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/harmonics-of-learning-universal-fourier#ran","syntology_url":"https://syntology.ai/paper/2312.08550","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.08550"}},"official":{"repos":["sophiaas/spectral-universality"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-to-augment-distributions-for-out-of-1","slug":"learning-to-augment-distributions-for-out-of-1","title":"Learning to Augment Distributions for Out-of-Distribution Detection","date":"2023-11-03","arxiv_id":"2311.01796","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-to-augment-distributions-for-out-of-1#ran","syntology_url":"https://syntology.ai/paper/2311.01796","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.01796"}},"official":{"repos":["tmlr-group/dal"],"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/why-do-we-need-weight-decay-in-modern-deep","slug":"why-do-we-need-weight-decay-in-modern-deep","title":"Why Do We Need Weight Decay in Modern Deep Learning?","date":"2023-10-06","arxiv_id":"2310.04415","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":2,"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/why-do-we-need-weight-decay-in-modern-deep#ran","syntology_url":"https://syntology.ai/paper/2310.04415","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.04415"}},"official":{"repos":["tml-epfl/why-weight-decay"],"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/spade-sparsity-guided-debugging-for-deep","slug":"spade-sparsity-guided-debugging-for-deep","title":"SPADE: Sparsity-Guided Debugging for Deep Neural Networks","date":"2023-10-06","arxiv_id":"2310.04519","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":9,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/spade-sparsity-guided-debugging-for-deep#ran","syntology_url":"https://syntology.ai/paper/2310.04519","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.04519"}},"official":{"repos":["ist-daslab/spade"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/quantifying-degeneracy-in-singular-models-via","slug":"quantifying-degeneracy-in-singular-models-via","title":"The Local Learning Coefficient: A Singularity-Aware Complexity Measure","date":"2023-08-23","arxiv_id":"2308.12108","repositories_listed":2,"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":8,"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/quantifying-degeneracy-in-singular-models-via#ran","syntology_url":"https://syntology.ai/paper/2308.12108","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12108"}},"official":{"repos":["edmundlth/scalable_learning_coefficient_with_sgld"],"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/deep-projection-networks-for-learning-time","slug":"deep-projection-networks-for-learning-time","title":"Learning invariant representations of time-homogeneous stochastic dynamical systems","date":"2023-07-19","arxiv_id":"2307.09912","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-projection-networks-for-learning-time#ran","syntology_url":"https://syntology.ai/paper/2307.09912","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.09912"}},"official":{"repos":["pietronvll/DPNets"],"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/how-does-information-bottleneck-help-deep","slug":"how-does-information-bottleneck-help-deep","title":"How Does Information Bottleneck Help Deep Learning?","date":"2023-05-30","arxiv_id":"2305.18887","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":3,"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) · 5 unverified","sample_list":"/paper/how-does-information-bottleneck-help-deep#ran","syntology_url":"https://syntology.ai/paper/2305.18887","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18887"}},"official":{"repos":["xu-ji/information-bottleneck"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/sampling-based-nystrom-approximation-and","slug":"sampling-based-nystrom-approximation-and","title":"Sampling-based Nyström Approximation and Kernel Quadrature","date":"2023-01-23","arxiv_id":"2301.09517","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/sampling-based-nystrom-approximation-and#ran","syntology_url":"https://syntology.ai/paper/2301.09517","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.09517"}},"official":{"repos":["satoshi-hayakawa/kernel-quadrature"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-neural-model-for-regular-grammar-induction","slug":"a-neural-model-for-regular-grammar-induction","title":"A Neural Model for Regular Grammar Induction","date":"2022-09-23","arxiv_id":"2209.11628","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/a-neural-model-for-regular-grammar-induction#ran","syntology_url":"https://syntology.ai/paper/2209.11628","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.11628"}},"official":{"repos":["pbelcak/neregrain"],"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/straggler-resilient-personalized-federated","slug":"straggler-resilient-personalized-federated","title":"Straggler-Resilient Personalized Federated Learning","date":"2022-06-05","arxiv_id":"2206.02078","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/straggler-resilient-personalized-federated#ran","syntology_url":"https://syntology.ai/paper/2206.02078","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.02078"}},"official":{"repos":["shenzebang/SRPFL"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/training-relu-networks-to-high-uniform","slug":"training-relu-networks-to-high-uniform","title":"Learning ReLU networks to high uniform accuracy is intractable","date":"2022-05-26","arxiv_id":"2205.13531","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/training-relu-networks-to-high-uniform#ran","syntology_url":"https://syntology.ai/paper/2205.13531","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.13531"}},"official":{"repos":["juliusberner/theory2practice"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pactran-pac-bayesian-metrics-for-estimating","slug":"pactran-pac-bayesian-metrics-for-estimating","title":"PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks","date":"2022-03-10","arxiv_id":"2203.05126","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pactran-pac-bayesian-metrics-for-estimating#ran","syntology_url":"https://syntology.ai/paper/2203.05126","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.05126"}},"official":{"repos":["google-research/pactran_metrics"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-structured-dictionary-perspective-on","slug":"a-structured-dictionary-perspective-on","title":"A Structured Dictionary Perspective on Implicit Neural Representations","date":"2021-12-03","arxiv_id":"2112.01917","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/a-structured-dictionary-perspective-on#ran","syntology_url":"https://syntology.ai/paper/2112.01917","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.01917"}},"official":{"repos":["gortizji/inr_dictionaries"],"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/intrinsic-dimension-persistent-homology-and-1","slug":"intrinsic-dimension-persistent-homology-and-1","title":"Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks","date":"2021-11-25","arxiv_id":"2111.13171","repositories_listed":2,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/intrinsic-dimension-persistent-homology-and-1#ran","syntology_url":"https://syntology.ai/paper/2111.13171","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.13171"}},"official":{"repos":["tolgabirdal/phdimgeneralization"],"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/causal-forecasting-generalization-bounds-for","slug":"causal-forecasting-generalization-bounds-for","title":"Causal Forecasting:Generalization Bounds for Autoregressive Models","date":"2021-11-18","arxiv_id":"2111.09831","repositories_listed":1,"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/causal-forecasting-generalization-bounds-for#ran","syntology_url":"https://syntology.ai/paper/2111.09831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.09831"}},"official":{"repos":["amazon-research/causal-forecasting"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/latent-equilibrium-a-unified-learning-theory","slug":"latent-equilibrium-a-unified-learning-theory","title":"Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons","date":"2021-10-27","arxiv_id":"2110.14549","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/latent-equilibrium-a-unified-learning-theory#ran","syntology_url":"https://syntology.ai/paper/2110.14549","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.14549"}},"official":null}},{"url":"/paper/on-optimal-interpolation-in-linear-regression","slug":"on-optimal-interpolation-in-linear-regression","title":"On Optimal Interpolation In Linear Regression","date":"2021-10-21","arxiv_id":"2110.11258","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/on-optimal-interpolation-in-linear-regression#ran","syntology_url":"https://syntology.ai/paper/2110.11258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.11258"}},"official":null}},{"url":"/paper/understanding-dimensional-collapse-in-1","slug":"understanding-dimensional-collapse-in-1","title":"Understanding Dimensional Collapse in Contrastive Self-supervised Learning","date":"2021-10-18","arxiv_id":"2110.09348","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/understanding-dimensional-collapse-in-1#ran","syntology_url":"https://syntology.ai/paper/2110.09348","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.09348"}},"official":{"repos":["facebookresearch/directclr"],"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","unlocated"]}}},{"url":"/paper/f-domain-adversarial-learning-theory-and-1","slug":"f-domain-adversarial-learning-theory-and-1","title":"f-Domain-Adversarial Learning: Theory and Algorithms","date":"2021-06-21","arxiv_id":"2106.11344","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"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) · 3 unverified","sample_list":"/paper/f-domain-adversarial-learning-theory-and-1#ran","syntology_url":"https://syntology.ai/paper/2106.11344","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.11344"}},"official":null}},{"url":"/paper/on-anytime-learning-at-macroscale","slug":"on-anytime-learning-at-macroscale","title":"On Anytime Learning at Macroscale","date":"2021-06-17","arxiv_id":"2106.09563","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"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) · 1 unverified","sample_list":"/paper/on-anytime-learning-at-macroscale#ran","syntology_url":"https://syntology.ai/paper/2106.09563","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.09563"}},"official":{"repos":["facebookresearch/alma"],"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/boosting-a-model-zoo-for-multi-task-and","slug":"boosting-a-model-zoo-for-multi-task-and","title":"Model Zoo: A Growing \"Brain\" That Learns Continually","date":"2021-06-06","arxiv_id":"2106.03027","repositories_listed":2,"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/boosting-a-model-zoo-for-multi-task-and#ran","syntology_url":"https://syntology.ai/paper/2106.03027","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03027"}},"official":{"repos":["grasp-lyrl/modelzoo_continual","rahul13ramesh/modelzoo_continual"],"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/deep-learning-is-singular-and-that-s-good-1","slug":"deep-learning-is-singular-and-that-s-good-1","title":"Deep Learning is Singular, and That's Good","date":"2020-10-22","arxiv_id":"2010.11560","repositories_listed":1,"syntology":{"n":17,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/deep-learning-is-singular-and-that-s-good-1#ran","syntology_url":"https://syntology.ai/paper/2010.11560","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.11560"}},"official":{"repos":["susanwe/RLCT"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/a-principle-of-least-action-for-the-training","slug":"a-principle-of-least-action-for-the-training","title":"A Principle of Least Action for the Training of Neural Networks","date":"2020-09-17","arxiv_id":"2009.08372","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/a-principle-of-least-action-for-the-training#ran","syntology_url":"https://syntology.ai/paper/2009.08372","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.08372"}},"official":{"repos":["skander-karkar/LAP"],"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":["official"]}}},{"url":"/paper/revisiting-complexity-and-the-bias-variance","slug":"revisiting-complexity-and-the-bias-variance","title":"Revisiting minimum description length complexity in overparameterized models","date":"2020-06-17","arxiv_id":"2006.10189","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/revisiting-complexity-and-the-bias-variance#ran","syntology_url":"https://syntology.ai/paper/2006.10189","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.10189"}},"official":{"repos":["csinva/mdl-complexity"],"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/rethink-the-connections-among-generalization","slug":"rethink-the-connections-among-generalization","title":"Rethink the Connections among Generalization, Memorization and the Spectral Bias of DNNs","date":"2020-04-29","arxiv_id":"2004.13954","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/rethink-the-connections-among-generalization#ran","syntology_url":"https://syntology.ai/paper/2004.13954","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.13954"}},"official":{"repos":["ZhangXiao96/RethinkSpectralBias"],"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/pac-confidence-sets-for-deep-neural-networks-1","slug":"pac-confidence-sets-for-deep-neural-networks-1","title":"PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction","date":"2019-12-31","arxiv_id":"2001.00106","repositories_listed":2,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/pac-confidence-sets-for-deep-neural-networks-1#ran","syntology_url":"https://syntology.ai/paper/2001.00106","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.00106"}},"official":{"repos":["sangdon/PAC-confidence-set"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/-net-systematic-evaluation-of-iterative-deep","slug":"-net-systematic-evaluation-of-iterative-deep","title":"$Σ$-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction","date":"2019-12-18","arxiv_id":"1912.09278","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/-net-systematic-evaluation-of-iterative-deep#ran","syntology_url":"https://syntology.ai/paper/1912.09278","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.09278"}},"official":{"repos":["khammernik/sigmanet"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/information-theoretic-local-minima-1","slug":"information-theoretic-local-minima-1","title":"Information-Theoretic Local Minima Characterization and Regularization","date":"2019-11-19","arxiv_id":"1911.08192","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/information-theoretic-local-minima-1#ran","syntology_url":"https://syntology.ai/paper/1911.08192","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.08192"}},"official":{"repos":["SeanJia/InfoMCR"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/truth-or-backpropaganda-an-empirical-1","slug":"truth-or-backpropaganda-an-empirical-1","title":"Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory","date":"2019-10-01","arxiv_id":"1910.00359","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/truth-or-backpropaganda-an-empirical-1#ran","syntology_url":"https://syntology.ai/paper/1910.00359","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.00359"}},"official":{"repos":["goldblum/TruthOrBackpropaganda"],"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/deep-learning-theory-review-an-optimal","slug":"deep-learning-theory-review-an-optimal","title":"Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective","date":"2019-08-28","arxiv_id":"1908.10920","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deep-learning-theory-review-an-optimal#ran","syntology_url":"https://syntology.ai/paper/1908.10920","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.10920"}},"official":{"repos":["ghliu/mean-field-fcdnn"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/evolving-and-understanding-sparse-deep-neural","slug":"evolving-and-understanding-sparse-deep-neural","title":"A Brain-inspired Algorithm for Training Highly Sparse Neural Networks","date":"2019-03-17","arxiv_id":"1903.07138","repositories_listed":2,"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":3,"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/evolving-and-understanding-sparse-deep-neural#ran","syntology_url":"https://syntology.ai/paper/1903.07138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.07138"}},"official":{"repos":["joostPieterse/CosineSET","zahraatashgahi/ctre"],"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/a-contextual-bandit-approach-to-personalized","slug":"a-contextual-bandit-approach-to-personalized","title":"A Contextual-Bandit Approach to Personalized News Article Recommendation","date":"2010-02-28","arxiv_id":"1003.0146","repositories_listed":12,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"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) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/a-contextual-bandit-approach-to-personalized#ran","syntology_url":"https://syntology.ai/paper/1003.0146","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1003.0146"}},"official":null}}],"record_sha256":"65b85b10a22bd898c5e1d7cb125e90e7b96d4735e3dc98577742ada8c921a275","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}