{"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/3","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":3,"pages_in_order":5,"rows_per_page":100,"rows":[201,300],"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/2","next":"/task/deep-learning/papers/ran/4","papers":[{"url":"/paper/quasi-global-momentum-accelerating","slug":"quasi-global-momentum-accelerating","title":"Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data","date":"2021-02-09","arxiv_id":"2102.04761","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/quasi-global-momentum-accelerating#ran","syntology_url":"https://syntology.ai/paper/2102.04761","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.04761"}},"official":{"repos":["epfml/quasi-global-momentum"],"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/protecting-intellectual-property-of","slug":"protecting-intellectual-property-of","title":"Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack","date":"2021-02-08","arxiv_id":"2102.04362","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"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) · 3 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/protecting-intellectual-property-of#ran","syntology_url":"https://syntology.ai/paper/2102.04362","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.04362"}},"official":{"repos":["dingsheng-ong/ipr-gan"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-time-attention-networks-for-irregularly-1","slug":"multi-time-attention-networks-for-irregularly-1","title":"Multi-Time Attention Networks for Irregularly Sampled Time Series","date":"2021-01-25","arxiv_id":"2101.10318","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"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 1 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/multi-time-attention-networks-for-irregularly-1#ran","syntology_url":"https://syntology.ai/paper/2101.10318","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.10318"}},"official":{"repos":["reml-lab/mTAN"],"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":["listed","official"]}}},{"url":"/paper/do-we-really-need-deep-learning-models-for","slug":"do-we-really-need-deep-learning-models-for","title":"Do We Really Need Deep Learning Models for Time Series Forecasting?","date":"2021-01-06","arxiv_id":"2101.02118","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/do-we-really-need-deep-learning-models-for#ran","syntology_url":"https://syntology.ai/paper/2101.02118","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.02118"}},"official":{"repos":["Daniela-Shereen/GBRT-for-TSF"],"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/ranking-deep-learning-generalization-using","slug":"ranking-deep-learning-generalization-using","title":"Ranking Deep Learning Generalization using Label Variation in Latent Geometry Graphs","date":"2020-11-25","arxiv_id":"2011.12737","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ranking-deep-learning-generalization-using#ran","syntology_url":"https://syntology.ai/paper/2011.12737","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.12737"}},"official":{"repos":["cadurosar/pgdl"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bridging-physics-based-and-data-driven","slug":"bridging-physics-based-and-data-driven","title":"Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems","date":"2020-11-20","arxiv_id":"2011.10616","repositories_listed":3,"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/bridging-physics-based-and-data-driven#ran","syntology_url":"https://syntology.ai/paper/2011.10616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.10616"}},"official":{"repos":["Rose-STL-Lab/AutoODE-DSL"],"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/efficient-variational-inference-for-sparse-1","slug":"efficient-variational-inference-for-sparse-1","title":"Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee","date":"2020-11-15","arxiv_id":"2011.07439","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":2,"n_ran_checked":3,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"6 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficient-variational-inference-for-sparse-1#ran","syntology_url":"https://syntology.ai/paper/2011.07439","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.07439"}},"official":{"repos":["JinchengBai/sparse-variational-bnn"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/solving-inverse-problems-with-deep-neural","slug":"solving-inverse-problems-with-deep-neural","title":"Solving Inverse Problems With Deep Neural Networks -- Robustness Included?","date":"2020-11-09","arxiv_id":"2011.04268","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":1,"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/solving-inverse-problems-with-deep-neural#ran","syntology_url":"https://syntology.ai/paper/2011.04268","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.04268"}},"official":{"repos":["jmaces/robust-nets"],"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","unlocated"]}}},{"url":"/paper/benchmarking-deep-learning-interpretability","slug":"benchmarking-deep-learning-interpretability","title":"Benchmarking Deep Learning Interpretability in Time Series Predictions","date":"2020-10-26","arxiv_id":"2010.13924","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 1 unverified","sample_list":"/paper/benchmarking-deep-learning-interpretability#ran","syntology_url":"https://syntology.ai/paper/2010.13924","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.13924"}},"official":{"repos":["ayaabdelsalam91/TS-Interpretability-Benchmark"],"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","unlocated"]}}},{"url":"/paper/multi-graph-tensor-networks","slug":"multi-graph-tensor-networks","title":"Multi-Graph Tensor Networks","date":"2020-10-25","arxiv_id":"2010.13209","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"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 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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/multi-graph-tensor-networks#ran","syntology_url":"https://syntology.ai/paper/2010.13209","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.13209"}},"official":{"repos":["gylx/GTNRL-Trading"],"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/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/stationary-activations-for-uncertainty","slug":"stationary-activations-for-uncertainty","title":"Stationary Activations for Uncertainty Calibration in Deep Learning","date":"2020-10-19","arxiv_id":"2010.09494","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/stationary-activations-for-uncertainty#ran","syntology_url":"https://syntology.ai/paper/2010.09494","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.09494"}},"official":{"repos":["AaltoML/stationary-activations"],"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/feature-importance-ranking-for-deep-learning","slug":"feature-importance-ranking-for-deep-learning","title":"Feature Importance Ranking for Deep Learning","date":"2020-10-18","arxiv_id":"2010.08973","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":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/feature-importance-ranking-for-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2010.08973","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.08973"}},"official":{"repos":["maksym33/FeatureImportanceDL"],"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/cs2-net-deep-learning-segmentation-of","slug":"cs2-net-deep-learning-segmentation-of","title":"CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging","date":"2020-10-15","arxiv_id":"2010.07486","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cs2-net-deep-learning-segmentation-of#ran","syntology_url":"https://syntology.ai/paper/2010.07486","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.07486"}},"official":{"repos":["iMED-Lab/CS-Net"],"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/revisiting-bfloat16-training","slug":"revisiting-bfloat16-training","title":"Revisiting BFloat16 Training","date":"2020-10-13","arxiv_id":"2010.06192","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"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; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/revisiting-bfloat16-training#ran","syntology_url":"https://syntology.ai/paper/2010.06192","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.06192"}},"official":null}},{"url":"/paper/empirical-frequentist-coverage-of-deep-1","slug":"empirical-frequentist-coverage-of-deep-1","title":"Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures","date":"2020-10-06","arxiv_id":"2010.03039","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/empirical-frequentist-coverage-of-deep-1#ran","syntology_url":"https://syntology.ai/paper/2010.03039","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.03039"}},"official":null}},{"url":"/paper/moldesigner-interactive-design-of-efficacious","slug":"moldesigner-interactive-design-of-efficacious","title":"MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning","date":"2020-10-05","arxiv_id":"2010.03951","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/moldesigner-interactive-design-of-efficacious#ran","syntology_url":"https://syntology.ai/paper/2010.03951","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.03951"}},"official":null}},{"url":"/paper/torchattacks-a-pytorch-repository-for","slug":"torchattacks-a-pytorch-repository-for","title":"Torchattacks: A PyTorch Repository for Adversarial Attacks","date":"2020-09-24","arxiv_id":"2010.01950","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":3,"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/torchattacks-a-pytorch-repository-for#ran","syntology_url":"https://syntology.ai/paper/2010.01950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.01950"}},"official":{"repos":["Harry24k/adversarial-attacks-pytorch"],"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/creation-and-validation-of-a-chest-x-ray","slug":"creation-and-validation-of-a-chest-x-ray","title":"Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Development","date":"2020-09-15","arxiv_id":"2009.07386","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/creation-and-validation-of-a-chest-x-ray#ran","syntology_url":"https://syntology.ai/paper/2009.07386","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.07386"}},"official":{"repos":["cxr-eye-gaze/eye-gaze-dataset"],"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/traces-of-class-cross-class-structure-pervade","slug":"traces-of-class-cross-class-structure-pervade","title":"Traces of Class/Cross-Class Structure Pervade Deep Learning Spectra","date":"2020-08-27","arxiv_id":"2008.11865","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/traces-of-class-cross-class-structure-pervade#ran","syntology_url":"https://syntology.ai/paper/2008.11865","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.11865"}},"official":null}},{"url":"/paper/hierarchical-deep-learning-of-multiscale","slug":"hierarchical-deep-learning-of-multiscale","title":"Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers","date":"2020-08-22","arxiv_id":"2008.09768","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/hierarchical-deep-learning-of-multiscale#ran","syntology_url":"https://syntology.ai/paper/2008.09768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.09768"}},"official":{"repos":["luckystarufo/multiscale_HiTS"],"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/skyline-interactive-in-editor-computational","slug":"skyline-interactive-in-editor-computational","title":"Skyline: Interactive In-Editor Computational Performance Profiling for Deep Neural Network Training","date":"2020-08-15","arxiv_id":"2008.06798","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/skyline-interactive-in-editor-computational#ran","syntology_url":"https://syntology.ai/paper/2008.06798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.06798"}},"official":{"repos":["skylineprof/skyline"],"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/deep-learning-to-quantify-pulmonary-edema-in","slug":"deep-learning-to-quantify-pulmonary-edema-in","title":"Deep Learning to Quantify Pulmonary Edema in Chest Radiographs","date":"2020-08-13","arxiv_id":"2008.05975","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"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) · 6 unverified","sample_list":"/paper/deep-learning-to-quantify-pulmonary-edema-in#ran","syntology_url":"https://syntology.ai/paper/2008.05975","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.05975"}},"official":{"repos":["RayRuizhiLiao/resnet_chestxray"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/complete-parameter-inference-for-gw150914","slug":"complete-parameter-inference-for-gw150914","title":"Complete parameter inference for GW150914 using deep learning","date":"2020-08-07","arxiv_id":"2008.03312","repositories_listed":2,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/complete-parameter-inference-for-gw150914#ran","syntology_url":"https://syntology.ai/paper/2008.03312","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.03312"}},"official":null}},{"url":"/paper/point-proposal-network-accelerating-point","slug":"point-proposal-network-accelerating-point","title":"Point Proposal Network: Accelerating Point Source Detection Through Deep Learning","date":"2020-08-05","arxiv_id":"2008.02093","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/point-proposal-network-accelerating-point#ran","syntology_url":"https://syntology.ai/paper/2008.02093","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.02093"}},"official":{"repos":["tilleyd/point-proposal-net"],"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/powergossip-practical-low-rank-communication","slug":"powergossip-practical-low-rank-communication","title":"PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning","date":"2020-08-04","arxiv_id":"2008.01425","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":2,"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/powergossip-practical-low-rank-communication#ran","syntology_url":"https://syntology.ai/paper/2008.01425","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.01425"}},"official":{"repos":["epfml/powergossip"],"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","unlocated"]}}},{"url":"/paper/self-supervised-learning-through-the-eyes-of","slug":"self-supervised-learning-through-the-eyes-of","title":"Self-supervised learning through the eyes of a child","date":"2020-07-31","arxiv_id":"2007.16189","repositories_listed":2,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"7 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; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/self-supervised-learning-through-the-eyes-of#ran","syntology_url":"https://syntology.ai/paper/2007.16189","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.16189"}},"official":{"repos":["eminorhan/baby-vision"],"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/human-trajectory-forecasting-in-crowds-a-deep","slug":"human-trajectory-forecasting-in-crowds-a-deep","title":"Human Trajectory Forecasting in Crowds: A Deep Learning Perspective","date":"2020-07-07","arxiv_id":"2007.03639","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/human-trajectory-forecasting-in-crowds-a-deep#ran","syntology_url":"https://syntology.ai/paper/2007.03639","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.03639"}},"official":null}},{"url":"/paper/deriving-neural-network-design-and-learning","slug":"deriving-neural-network-design-and-learning","title":"A Chain Graph Interpretation of Real-World Neural Networks","date":"2020-06-30","arxiv_id":"2006.16856","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"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) · 3 unverified","sample_list":"/paper/deriving-neural-network-design-and-learning#ran","syntology_url":"https://syntology.ai/paper/2006.16856","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.16856"}},"official":{"repos":["tum-vision/nnascg"],"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/normalized-loss-functions-for-deep-learning","slug":"normalized-loss-functions-for-deep-learning","title":"Normalized Loss Functions for Deep Learning with Noisy Labels","date":"2020-06-24","arxiv_id":"2006.13554","repositories_listed":4,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/normalized-loss-functions-for-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2006.13554","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.13554"}},"official":{"repos":["HanxunH/Active-Passive-Losses"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/direct-feedback-alignment-scales-to-modern","slug":"direct-feedback-alignment-scales-to-modern","title":"Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures","date":"2020-06-23","arxiv_id":"2006.12878","repositories_listed":1,"syntology":{"n":7,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/direct-feedback-alignment-scales-to-modern#ran","syntology_url":"https://syntology.ai/paper/2006.12878","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12878"}},"official":{"repos":["lightonai/dfa-scales-to-modern-deep-learning"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-neural-networks-in-tensorflow-and-keras","slug":"graph-neural-networks-in-tensorflow-and-keras","title":"Graph Neural Networks in TensorFlow and Keras with Spektral","date":"2020-06-22","arxiv_id":"2006.12138","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/graph-neural-networks-in-tensorflow-and-keras#ran","syntology_url":"https://syntology.ai/paper/2006.12138","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12138"}},"official":{"repos":["danielegrattarola/spektral"],"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/aradic-arabic-document-classification-using-1","slug":"aradic-arabic-document-classification-using-1","title":"AraDIC: Arabic Document Classification using Image-Based Character Embeddings and Class-Balanced Loss","date":"2020-06-20","arxiv_id":"2006.11586","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"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 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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/aradic-arabic-document-classification-using-1#ran","syntology_url":"https://syntology.ai/paper/2006.11586","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.11586"}},"official":{"repos":["mahmouddaif/AraDIC"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/discovering-symbolic-models-from-deep","slug":"discovering-symbolic-models-from-deep","title":"Discovering Symbolic Models from Deep Learning with Inductive Biases","date":"2020-06-19","arxiv_id":"2006.11287","repositories_listed":3,"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/discovering-symbolic-models-from-deep#ran","syntology_url":"https://syntology.ai/paper/2006.11287","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.11287"}},"official":{"repos":["MilesCranmer/PySR","MilesCranmer/symbolic_deep_learning"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/temporal-graph-networks-for-deep-learning-on","slug":"temporal-graph-networks-for-deep-learning-on","title":"Temporal Graph Networks for Deep Learning on Dynamic Graphs","date":"2020-06-18","arxiv_id":"2006.10637","repositories_listed":10,"syntology":{"n":19,"n_ran":11,"n_constructed":0,"n_ran_checked":7,"n_instrument":4,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"11 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; 4 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/temporal-graph-networks-for-deep-learning-on#ran","syntology_url":"https://syntology.ai/paper/2006.10637","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.10637"}},"official":null}},{"url":"/paper/markov-lipschitz-deep-learning","slug":"markov-lipschitz-deep-learning","title":"Markov-Lipschitz Deep Learning","date":"2020-06-15","arxiv_id":"2006.08256","repositories_listed":2,"syntology":{"n":7,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/markov-lipschitz-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2006.08256","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.08256"}},"official":{"repos":["westlake-cairi/Markov-Lipschitz-Deep-Learning"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/explaining-local-global-and-higher-order","slug":"explaining-local-global-and-higher-order","title":"Explaining Local, Global, And Higher-Order Interactions In Deep Learning","date":"2020-06-12","arxiv_id":"2006.08601","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/explaining-local-global-and-higher-order#ran","syntology_url":"https://syntology.ai/paper/2006.08601","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.08601"}},"official":{"repos":["slerman12/ExplainingInteractions"],"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-importance-weighting-for-deep","slug":"rethinking-importance-weighting-for-deep","title":"Rethinking Importance Weighting for Deep Learning under Distribution Shift","date":"2020-06-08","arxiv_id":"2006.04662","repositories_listed":1,"syntology":{"n":12,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":8,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"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) · 8 unverified","sample_list":"/paper/rethinking-importance-weighting-for-deep#ran","syntology_url":"https://syntology.ai/paper/2006.04662","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.04662"}},"official":{"repos":["TongtongFANG/DIW"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":8,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/emergent-multi-agent-communication-in-the","slug":"emergent-multi-agent-communication-in-the","title":"Emergent Multi-Agent Communication in the Deep Learning Era","date":"2020-06-03","arxiv_id":"2006.02419","repositories_listed":1,"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/emergent-multi-agent-communication-in-the#ran","syntology_url":"https://syntology.ai/paper/2006.02419","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.02419"}},"official":null}},{"url":"/paper/deep-learning-for-automatic-pneumonia","slug":"deep-learning-for-automatic-pneumonia","title":"Deep Learning for Automatic Pneumonia Detection","date":"2020-05-28","arxiv_id":"2005.13899","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-learning-for-automatic-pneumonia#ran","syntology_url":"https://syntology.ai/paper/2005.13899","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.13899"}},"official":{"repos":["tatigabru/kaggle-rsna"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/representation-learning-with-fine-grained","slug":"representation-learning-with-fine-grained","title":"Weakly Supervised Representation Learning with Coarse Labels","date":"2020-05-19","arxiv_id":"2005.09681","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":4,"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) · 0 unverified","sample_list":"/paper/representation-learning-with-fine-grained#ran","syntology_url":"https://syntology.ai/paper/2005.09681","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.09681"}},"official":{"repos":["idstcv/coins"],"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","unlocated"]}}},{"url":"/paper/stillleben-realistic-scene-synthesis-for-deep","slug":"stillleben-realistic-scene-synthesis-for-deep","title":"Stillleben: Realistic Scene Synthesis for Deep Learning in Robotics","date":"2020-05-12","arxiv_id":"2005.05659","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/stillleben-realistic-scene-synthesis-for-deep#ran","syntology_url":"https://syntology.ai/paper/2005.05659","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.05659"}},"official":{"repos":["AIS-Bonn/stillleben"],"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/blind-backdoors-in-deep-learning-models","slug":"blind-backdoors-in-deep-learning-models","title":"Blind Backdoors in Deep Learning Models","date":"2020-05-08","arxiv_id":"2005.03823","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"8 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/blind-backdoors-in-deep-learning-models#ran","syntology_url":"https://syntology.ai/paper/2005.03823","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.03823"}},"official":{"repos":["ebagdasa/backdoors101"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cnn-explainer-learning-convolutional-neural","slug":"cnn-explainer-learning-convolutional-neural","title":"CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization","date":"2020-04-30","arxiv_id":"2004.15004","repositories_listed":5,"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/cnn-explainer-learning-convolutional-neural#ran","syntology_url":"https://syntology.ai/paper/2004.15004","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.15004"}},"official":{"repos":["poloclub/cnn-explainer"],"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/neural-forecasting-introduction-and","slug":"neural-forecasting-introduction-and","title":"Deep Learning for Time Series Forecasting: Tutorial and Literature Survey","date":"2020-04-21","arxiv_id":"2004.10240","repositories_listed":2,"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/neural-forecasting-introduction-and#ran","syntology_url":"https://syntology.ai/paper/2004.10240","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.10240"}},"official":null}},{"url":"/paper/2003-13479","slug":"2003-13479","title":"RPM-Net: Robust Point Matching using Learned Features","date":"2020-03-30","arxiv_id":"2003.13479","repositories_listed":5,"syntology":{"n":31,"n_ran":26,"n_constructed":0,"n_ran_checked":25,"n_instrument":1,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":24,"n_pointer_only":16,"phrase":"26 ran (of which 0 constructed an object rather than computing a result; 25 with no instrument failure: 1 honoured, 0 violated, 24 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/2003-13479#ran","syntology_url":"https://syntology.ai/paper/2003.13479","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.13479"}},"official":{"repos":["yewzijian/RPMNet"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/deep-graph-matching-via-blackbox","slug":"deep-graph-matching-via-blackbox","title":"Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers","date":"2020-03-25","arxiv_id":"2003.11657","repositories_listed":5,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":6,"n_instrument":4,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":5,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deep-graph-matching-via-blackbox#ran","syntology_url":"https://syntology.ai/paper/2003.11657","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.11657"}},"official":{"repos":["martius-lab/blackbox-deep-graph-matching"],"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/tf-coder-program-synthesis-for-tensor","slug":"tf-coder-program-synthesis-for-tensor","title":"TF-Coder: Program Synthesis for Tensor Manipulations","date":"2020-03-19","arxiv_id":"2003.09040","repositories_listed":2,"syntology":{"n":14,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":6,"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) · 6 unverified","sample_list":"/paper/tf-coder-program-synthesis-for-tensor#ran","syntology_url":"https://syntology.ai/paper/2003.09040","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.09040"}},"official":{"repos":["google-research/tensorflow-coder"],"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":["listed","official"]}}},{"url":"/paper/mnn-a-universal-and-efficient-inference","slug":"mnn-a-universal-and-efficient-inference","title":"MNN: A Universal and Efficient Inference Engine","date":"2020-02-27","arxiv_id":"2002.12418","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/mnn-a-universal-and-efficient-inference#ran","syntology_url":"https://syntology.ai/paper/2002.12418","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.12418"}},"official":{"repos":["alibaba/MNN"],"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/overfitting-in-adversarially-robust-deep","slug":"overfitting-in-adversarially-robust-deep","title":"Overfitting in adversarially robust deep learning","date":"2020-02-26","arxiv_id":"2002.11569","repositories_listed":4,"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/overfitting-in-adversarially-robust-deep#ran","syntology_url":"https://syntology.ai/paper/2002.11569","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.11569"}},"official":{"repos":["locuslab/robust_overfitting"],"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/chemgrapher-optical-graph-recognition-of","slug":"chemgrapher-optical-graph-recognition-of","title":"ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning","date":"2020-02-23","arxiv_id":"2002.09914","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/chemgrapher-optical-graph-recognition-of#ran","syntology_url":"https://syntology.ai/paper/2002.09914","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.09914"}},"official":null}},{"url":"/paper/fawkes-protecting-personal-privacy-against","slug":"fawkes-protecting-personal-privacy-against","title":"Fawkes: Protecting Privacy against Unauthorized Deep Learning Models","date":"2020-02-19","arxiv_id":"2002.08327","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/fawkes-protecting-personal-privacy-against#ran","syntology_url":"https://syntology.ai/paper/2002.08327","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.08327"}},"official":{"repos":["Shawn-Shan/fawkes"],"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/how-to-0wn-nas-in-your-spare-time","slug":"how-to-0wn-nas-in-your-spare-time","title":"How to 0wn NAS in Your Spare Time","date":"2020-02-17","arxiv_id":"2002.06776","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/how-to-0wn-nas-in-your-spare-time#ran","syntology_url":"https://syntology.ai/paper/2002.06776","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.06776"}},"official":{"repos":["Sanghyun-Hong/How-to-0wn-NAS-in-Your-Spare-Time"],"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/combining-schnet-and-sharc-the-schnarc","slug":"combining-schnet-and-sharc-the-schnarc","title":"Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics","date":"2020-02-17","arxiv_id":"2002.07264","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/combining-schnet-and-sharc-the-schnarc#ran","syntology_url":"https://syntology.ai/paper/2002.07264","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.07264"}},"official":{"repos":["schnarc/schnarc"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/adversarial-distributional-training-for","slug":"adversarial-distributional-training-for","title":"Adversarial Distributional Training for Robust Deep Learning","date":"2020-02-14","arxiv_id":"2002.05999","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":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) · 1 unverified","sample_list":"/paper/adversarial-distributional-training-for#ran","syntology_url":"https://syntology.ai/paper/2002.05999","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.05999"}},"official":{"repos":["dongyp13/Adversarial-Distributional-Training"],"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/scalable-and-practical-natural-gradient-for","slug":"scalable-and-practical-natural-gradient-for","title":"Scalable and Practical Natural Gradient for Large-Scale Deep Learning","date":"2020-02-13","arxiv_id":"2002.06015","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/scalable-and-practical-natural-gradient-for#ran","syntology_url":"https://syntology.ai/paper/2002.06015","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.06015"}},"official":{"repos":["tyohei/chainerkfac"],"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/shortest-path-distance-approximation-using","slug":"shortest-path-distance-approximation-using","title":"Shortest path distance approximation using deep learning techniques","date":"2020-02-12","arxiv_id":"2002.05257","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/shortest-path-distance-approximation-using#ran","syntology_url":"https://syntology.ai/paper/2002.05257","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.05257"}},"official":null}},{"url":"/paper/fastai-a-layered-api-for-deep-learning","slug":"fastai-a-layered-api-for-deep-learning","title":"fastai: A Layered API for Deep Learning","date":"2020-02-11","arxiv_id":"2002.04688","repositories_listed":2,"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":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) · 7 unverified","sample_list":"/paper/fastai-a-layered-api-for-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2002.04688","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.04688"}},"official":{"repos":["fastai/fastai"],"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/the-deep-learning-compiler-a-comprehensive","slug":"the-deep-learning-compiler-a-comprehensive","title":"The Deep Learning Compiler: A Comprehensive Survey","date":"2020-02-06","arxiv_id":"2002.03794","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/the-deep-learning-compiler-a-comprehensive#ran","syntology_url":"https://syntology.ai/paper/2002.03794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.03794"}},"official":{"repos":["buaa-hipo/dlcompiler-comparison"],"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-person-re-identification-a","slug":"deep-learning-for-person-re-identification-a","title":"Deep Learning for Person Re-identification: A Survey and Outlook","date":"2020-01-13","arxiv_id":"2001.04193","repositories_listed":7,"syntology":{"n":22,"n_ran":19,"n_constructed":0,"n_ran_checked":13,"n_instrument":6,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":12,"n_pointer_only":5,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 1 violated, 12 with no contract checked; 6 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/deep-learning-for-person-re-identification-a#ran","syntology_url":"https://syntology.ai/paper/2001.04193","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.04193"}},"official":{"repos":["mangye16/ReID-Survey"],"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","unlocated"]}}},{"url":"/paper/identifying-and-compensating-for-feature","slug":"identifying-and-compensating-for-feature","title":"Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning","date":"2020-01-06","arxiv_id":"2001.01385","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/identifying-and-compensating-for-feature#ran","syntology_url":"https://syntology.ai/paper/2001.01385","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.01385"}},"official":null}},{"url":"/paper/addernet-do-we-really-need-multiplications-in","slug":"addernet-do-we-really-need-multiplications-in","title":"AdderNet: Do We Really Need Multiplications in Deep Learning?","date":"2019-12-31","arxiv_id":"1912.13200","repositories_listed":7,"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":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) · 0 unverified","sample_list":"/paper/addernet-do-we-really-need-multiplications-in#ran","syntology_url":"https://syntology.ai/paper/1912.13200","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.13200"}},"official":{"repos":["huawei-noah/AdderNet"],"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/combining-deep-learning-and-verification-for","slug":"combining-deep-learning-and-verification-for","title":"Combining Deep Learning and Verification for Precise Object Instance Detection","date":"2019-12-27","arxiv_id":"1912.12270","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":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/combining-deep-learning-and-verification-for#ran","syntology_url":"https://syntology.ai/paper/1912.12270","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.12270"}},"official":{"repos":["siddancha/FlowVerify"],"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/assessing-differentially-private-deep","slug":"assessing-differentially-private-deep","title":"Assessing differentially private deep learning with Membership Inference","date":"2019-12-24","arxiv_id":"1912.11328","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/assessing-differentially-private-deep#ran","syntology_url":"https://syntology.ai/paper/1912.11328","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.11328"}},"official":{"repos":["SAP-samples/security-research-membership-inference-and-differential-privacy"],"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/deep-learning-for-symbolic-mathematics-1","slug":"deep-learning-for-symbolic-mathematics-1","title":"Deep Learning for Symbolic Mathematics","date":"2019-12-02","arxiv_id":"1912.01412","repositories_listed":7,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":2,"n_instrument":4,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"6 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; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deep-learning-for-symbolic-mathematics-1#ran","syntology_url":"https://syntology.ai/paper/1912.01412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.01412"}},"official":null}},{"url":"/paper/deep-learning-with-gaussian-differential","slug":"deep-learning-with-gaussian-differential","title":"Deep Learning with Gaussian Differential Privacy","date":"2019-11-26","arxiv_id":"1911.11607","repositories_listed":3,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":3,"n_no_contract":0,"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, 3 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deep-learning-with-gaussian-differential#ran","syntology_url":"https://syntology.ai/paper/1911.11607","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.11607"}},"official":{"repos":["woodyx218/Deep-Learning-with-GDP"],"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/towards-physics-informed-deep-learning-for-1","slug":"towards-physics-informed-deep-learning-for-1","title":"Towards Physics-informed Deep Learning for Turbulent Flow Prediction","date":"2019-11-20","arxiv_id":"1911.08655","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"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) · 2 unverified","sample_list":"/paper/towards-physics-informed-deep-learning-for-1#ran","syntology_url":"https://syntology.ai/paper/1911.08655","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.08655"}},"official":null}},{"url":"/paper/improving-graph-neural-network","slug":"improving-graph-neural-network","title":"Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling","date":"2019-11-15","arxiv_id":"1911.06904","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/improving-graph-neural-network#ran","syntology_url":"https://syntology.ai/paper/1911.06904","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.06904"}},"official":null}},{"url":"/paper/kaolin-a-pytorch-library-for-accelerating-3d","slug":"kaolin-a-pytorch-library-for-accelerating-3d","title":"Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research","date":"2019-11-12","arxiv_id":"1911.05063","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":1,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/kaolin-a-pytorch-library-for-accelerating-3d#ran","syntology_url":"https://syntology.ai/paper/1911.05063","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.05063"}},"official":{"repos":["NVIDIAGameWorks/kaolin"],"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/u-time-a-fully-convolutional-network-for-time","slug":"u-time-a-fully-convolutional-network-for-time","title":"U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging","date":"2019-10-24","arxiv_id":"1910.11162","repositories_listed":5,"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":2,"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/u-time-a-fully-convolutional-network-for-time#ran","syntology_url":"https://syntology.ai/paper/1910.11162","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.11162"}},"official":null}},{"url":"/paper/self-attention-for-raw-optical-satellite-time","slug":"self-attention-for-raw-optical-satellite-time","title":"Self-attention for raw optical Satellite Time Series Classification","date":"2019-10-23","arxiv_id":"1910.10536","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":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/self-attention-for-raw-optical-satellite-time#ran","syntology_url":"https://syntology.ai/paper/1910.10536","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.10536"}},"official":{"repos":["marccoru/crop-type-mapping"],"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/an-adaptive-empirical-bayesian-method-for","slug":"an-adaptive-empirical-bayesian-method-for","title":"An Adaptive Empirical Bayesian Method for Sparse Deep Learning","date":"2019-10-23","arxiv_id":"1910.10791","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/an-adaptive-empirical-bayesian-method-for#ran","syntology_url":"https://syntology.ai/paper/1910.10791","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.10791"}},"official":null}},{"url":"/paper/complex-transformer-a-framework-for-modeling","slug":"complex-transformer-a-framework-for-modeling","title":"Complex Transformer: A Framework for Modeling Complex-Valued Sequence","date":"2019-10-22","arxiv_id":"1910.10202","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/complex-transformer-a-framework-for-modeling#ran","syntology_url":"https://syntology.ai/paper/1910.10202","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.10202"}},"official":{"repos":["muqiaoy/dl_signal"],"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/meteornet-deep-learning-on-dynamic-3d-point","slug":"meteornet-deep-learning-on-dynamic-3d-point","title":"MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences","date":"2019-10-21","arxiv_id":"1910.09165","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/meteornet-deep-learning-on-dynamic-3d-point#ran","syntology_url":"https://syntology.ai/paper/1910.09165","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.09165"}},"official":{"repos":["xingyul/meteornet"],"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/surreal-complex-valued-deep-learning-as","slug":"surreal-complex-valued-deep-learning-as","title":"SurReal: Complex-Valued Learning as Principled Transformations on a Scaling and Rotation Manifold","date":"2019-10-18","arxiv_id":"1910.11334","repositories_listed":1,"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":5,"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/surreal-complex-valued-deep-learning-as#ran","syntology_url":"https://syntology.ai/paper/1910.11334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.11334"}},"official":null}},{"url":"/paper/fastsurfer-a-fast-and-accurate-deep-learning","slug":"fastsurfer-a-fast-and-accurate-deep-learning","title":"FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline","date":"2019-10-09","arxiv_id":"1910.03866","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/fastsurfer-a-fast-and-accurate-deep-learning#ran","syntology_url":"https://syntology.ai/paper/1910.03866","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.03866"}},"official":{"repos":["Deep-MI/FastSurfer"],"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/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/interpretations-are-useful-penalizing","slug":"interpretations-are-useful-penalizing","title":"Interpretations are useful: penalizing explanations to align neural networks with prior knowledge","date":"2019-09-30","arxiv_id":"1909.13584","repositories_listed":4,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":1,"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; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/interpretations-are-useful-penalizing#ran","syntology_url":"https://syntology.ai/paper/1909.13584","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.13584"}},"official":{"repos":["laura-rieger/deep-explanation-penalization"],"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":["listed","official"]}}},{"url":"/paper/neural-oblivious-decision-ensembles-for-deep","slug":"neural-oblivious-decision-ensembles-for-deep","title":"Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data","date":"2019-09-13","arxiv_id":"1909.06312","repositories_listed":5,"syntology":{"n":7,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/neural-oblivious-decision-ensembles-for-deep#ran","syntology_url":"https://syntology.ai/paper/1909.06312","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.06312"}},"official":{"repos":["Qwicen/node","anonICLR2020/node"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/interpolation-prediction-networks-for-1","slug":"interpolation-prediction-networks-for-1","title":"Interpolation-Prediction Networks for Irregularly Sampled Time Series","date":"2019-09-13","arxiv_id":"1909.07782","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/interpolation-prediction-networks-for-1#ran","syntology_url":"https://syntology.ai/paper/1909.07782","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.07782"}},"official":{"repos":["mlds-lab/interp-net"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dreamtime-finding-alexnet-for-time-series","slug":"dreamtime-finding-alexnet-for-time-series","title":"InceptionTime: Finding AlexNet for Time Series Classification","date":"2019-09-11","arxiv_id":"1909.04939","repositories_listed":10,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dreamtime-finding-alexnet-for-time-series#ran","syntology_url":"https://syntology.ai/paper/1909.04939","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.04939"}},"official":{"repos":["hfawaz/InceptionTime"],"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","unlocated"]}}},{"url":"/paper/beyond-human-level-accuracy-computational","slug":"beyond-human-level-accuracy-computational","title":"Beyond Human-Level Accuracy: Computational Challenges in Deep Learning","date":"2019-09-03","arxiv_id":"1909.01736","repositories_listed":1,"syntology":{"n":9,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/beyond-human-level-accuracy-computational#ran","syntology_url":"https://syntology.ai/paper/1909.01736","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.01736"}},"official":{"repos":["baidu-research/catamount"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/o-medal-online-active-deep-learning-for","slug":"o-medal-online-active-deep-learning-for","title":"O-MedAL: Online Active Deep Learning for Medical Image Analysis","date":"2019-08-28","arxiv_id":"1908.10508","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/o-medal-online-active-deep-learning-for#ran","syntology_url":"https://syntology.ai/paper/1908.10508","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.10508"}},"official":{"repos":["adgaudio/O-MedAL"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"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/rotation-invariant-convolutions-for-3d-point","slug":"rotation-invariant-convolutions-for-3d-point","title":"Rotation Invariant Convolutions for 3D Point Clouds Deep Learning","date":"2019-08-17","arxiv_id":"1908.06297","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":1,"n_violates":0,"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: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/rotation-invariant-convolutions-for-3d-point#ran","syntology_url":"https://syntology.ai/paper/1908.06297","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.06297"}},"official":{"repos":["hkust-vgd/riconv"],"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/the-hsic-bottleneck-deep-learning-without","slug":"the-hsic-bottleneck-deep-learning-without","title":"The HSIC Bottleneck: Deep Learning without Back-Propagation","date":"2019-08-05","arxiv_id":"1908.01580","repositories_listed":3,"syntology":{"n":16,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":12,"n_honours":2,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 12 unverified","sample_list":"/paper/the-hsic-bottleneck-deep-learning-without#ran","syntology_url":"https://syntology.ai/paper/1908.01580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.01580"}},"official":{"repos":["choasma/HSIC-Bottleneck"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":10,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/asnets-deep-learning-for-generalised-planning","slug":"asnets-deep-learning-for-generalised-planning","title":"ASNets: Deep Learning for Generalised Planning","date":"2019-08-04","arxiv_id":"1908.01362","repositories_listed":1,"syntology":{"n":18,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"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) · 6 unverified","sample_list":"/paper/asnets-deep-learning-for-generalised-planning#ran","syntology_url":"https://syntology.ai/paper/1908.01362","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.01362"}},"official":{"repos":["qxcv/asnets"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-neural-network-hyperparameter","slug":"deep-neural-network-hyperparameter","title":"Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning","date":"2019-07-31","arxiv_id":"1907.13359","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-neural-network-hyperparameter#ran","syntology_url":"https://syntology.ai/paper/1907.13359","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.13359"}},"official":{"repos":["xiangzhang1015/OATM"],"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/benchmarking-tpu-gpu-and-cpu-platforms-for","slug":"benchmarking-tpu-gpu-and-cpu-platforms-for","title":"Benchmarking TPU, GPU, and CPU Platforms for Deep Learning","date":"2019-07-24","arxiv_id":"1907.10701","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/benchmarking-tpu-gpu-and-cpu-platforms-for#ran","syntology_url":"https://syntology.ai/paper/1907.10701","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.10701"}},"official":{"repos":["Emma926/paradnn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-learning-for-time-series-forecasting-the","slug":"deep-learning-for-time-series-forecasting-the","title":"Deep Learning for Time Series Forecasting: The Electric Load Case","date":"2019-07-22","arxiv_id":"1907.09207","repositories_listed":2,"syntology":{"n":10,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":7,"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) · 7 unverified","sample_list":"/paper/deep-learning-for-time-series-forecasting-the#ran","syntology_url":"https://syntology.ai/paper/1907.09207","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.09207"}},"official":null}},{"url":"/paper/decentralized-deep-learning-with-arbitrary","slug":"decentralized-deep-learning-with-arbitrary","title":"Decentralized Deep Learning with Arbitrary Communication Compression","date":"2019-07-22","arxiv_id":"1907.09356","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":4,"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, 1 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/decentralized-deep-learning-with-arbitrary#ran","syntology_url":"https://syntology.ai/paper/1907.09356","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.09356"}},"official":{"repos":["epfml/ChocoSGD"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deepcr-cosmic-ray-rejection-with-deep","slug":"deepcr-cosmic-ray-rejection-with-deep","title":"deepCR: Cosmic Ray Rejection with Deep Learning","date":"2019-07-22","arxiv_id":"1907.09500","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/deepcr-cosmic-ray-rejection-with-deep#ran","syntology_url":"https://syntology.ai/paper/1907.09500","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.09500"}},"official":{"repos":["profjsb/deepCR","kmzzhang/deepCR-paper"],"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/green-ai","slug":"green-ai","title":"Green AI","date":"2019-07-22","arxiv_id":"1907.10597","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/green-ai#ran","syntology_url":"https://syntology.ai/paper/1907.10597","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.10597"}},"official":null}},{"url":"/paper/vs-net-variable-splitting-network-for","slug":"vs-net-variable-splitting-network-for","title":"VS-Net: Variable splitting network for accelerated parallel MRI reconstruction","date":"2019-07-19","arxiv_id":"1907.10033","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/vs-net-variable-splitting-network-for#ran","syntology_url":"https://syntology.ai/paper/1907.10033","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.10033"}},"official":{"repos":["j-duan/VS-Net"],"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/subspace-inference-for-bayesian-deep-learning","slug":"subspace-inference-for-bayesian-deep-learning","title":"Subspace Inference for Bayesian Deep Learning","date":"2019-07-17","arxiv_id":"1907.07504","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":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) · 1 unverified","sample_list":"/paper/subspace-inference-for-bayesian-deep-learning#ran","syntology_url":"https://syntology.ai/paper/1907.07504","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.07504"}},"official":{"repos":["wjmaddox/drbayes"],"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-general-framework-for-uncertainty","slug":"a-general-framework-for-uncertainty","title":"A General Framework for Uncertainty Estimation in Deep Learning","date":"2019-07-16","arxiv_id":"1907.06890","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":2,"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) · 2 unverified","sample_list":"/paper/a-general-framework-for-uncertainty#ran","syntology_url":"https://syntology.ai/paper/1907.06890","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.06890"}},"official":{"repos":["mattiasegu/uncertainty_estimation_deep_learning"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptive-deep-learning-for-high-dimensional","slug":"adaptive-deep-learning-for-high-dimensional","title":"Adaptive Deep Learning for High-Dimensional Hamilton-Jacobi-Bellman Equations","date":"2019-07-11","arxiv_id":"1907.05317","repositories_listed":1,"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/adaptive-deep-learning-for-high-dimensional#ran","syntology_url":"https://syntology.ai/paper/1907.05317","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.05317"}},"official":null}},{"url":"/paper/deep-lagrangian-networks-using-physics-as-1","slug":"deep-lagrangian-networks-using-physics-as-1","title":"Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning","date":"2019-07-10","arxiv_id":"1907.04490","repositories_listed":3,"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-lagrangian-networks-using-physics-as-1#ran","syntology_url":"https://syntology.ai/paper/1907.04490","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.04490"}},"official":null}},{"url":"/paper/deepxde-a-deep-learning-library-for-solving","slug":"deepxde-a-deep-learning-library-for-solving","title":"DeepXDE: A deep learning library for solving differential equations","date":"2019-07-10","arxiv_id":"1907.04502","repositories_listed":7,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":11,"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, 1 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deepxde-a-deep-learning-library-for-solving#ran","syntology_url":"https://syntology.ai/paper/1907.04502","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.04502"}},"official":{"repos":["lululxvi/deepxde"],"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/multivariate-time-series-imputation-with-1","slug":"multivariate-time-series-imputation-with-1","title":"GP-VAE: Deep Probabilistic Time Series Imputation","date":"2019-07-09","arxiv_id":"1907.04155","repositories_listed":4,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/multivariate-time-series-imputation-with-1#ran","syntology_url":"https://syntology.ai/paper/1907.04155","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.04155"}},"official":{"repos":["ratschlab/GP-VAE"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}}],"record_sha256":"ae817d74c9405fff9d38a1f0c0303443d5324832675cfe7ce4a4f0cb976837c5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}