{"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/representation-learning/papers/ran/14","list_of":"/task/representation-learning","task":"Representation 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":14,"pages_in_order":15,"rows_per_page":100,"rows":[1301,1400],"of":1439,"counts":{"archive_papers_tagged":10580,"with_a_code_link":4662,"where_syntology_ran_a_sample":1439,"not_listed_spam_title":0,"listed":10580,"listed_where_code_ran":1439,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1228,"every_run_a_failure_of_syntologys_instrument":211,"listed_with_a_run_with_no_instrument_failure":1228,"listed_every_run_a_failure_of_syntologys_instrument":211,"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/representation-learning/papers/ran/1","prev":"/task/representation-learning/papers/ran/13","next":"/task/representation-learning/papers/ran/15","papers":[{"url":"/paper/function-space-distributions-over-kernels","slug":"function-space-distributions-over-kernels","title":"Function-Space Distributions over Kernels","date":"2019-10-29","arxiv_id":"1910.13565","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/function-space-distributions-over-kernels#ran","syntology_url":"https://syntology.ai/paper/1910.13565","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.13565"}},"official":{"repos":["wjmaddox/spectralgp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/mockingjay-unsupervised-speech-representation","slug":"mockingjay-unsupervised-speech-representation","title":"Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders","date":"2019-10-25","arxiv_id":"1910.12638","repositories_listed":7,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":3,"n_honours":1,"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; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/mockingjay-unsupervised-speech-representation#ran","syntology_url":"https://syntology.ai/paper/1910.12638","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.12638"}},"official":{"repos":["andi611/Self-Supervised-Speech-Pretraining-and-Representation-Learning"],"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/hierarchical-representation-learning-in-graph","slug":"hierarchical-representation-learning-in-graph","title":"Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling","date":"2019-10-24","arxiv_id":"1910.11436","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/hierarchical-representation-learning-in-graph#ran","syntology_url":"https://syntology.ai/paper/1910.11436","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.11436"}},"official":{"repos":["danielegrattarola/decimation-pooling"],"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/gradslam-dense-slam-meets-automatic","slug":"gradslam-dense-slam-meets-automatic","title":"gradSLAM: Automagically differentiable SLAM","date":"2019-10-23","arxiv_id":"1910.10672","repositories_listed":1,"syntology":{"n":15,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/gradslam-dense-slam-meets-automatic#ran","syntology_url":"https://syntology.ai/paper/1910.10672","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.10672"}},"official":{"repos":["gradslam/gradslam"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/decoupling-representation-and-classifier-for","slug":"decoupling-representation-and-classifier-for","title":"Decoupling Representation and Classifier for Long-Tailed Recognition","date":"2019-10-21","arxiv_id":"1910.09217","repositories_listed":4,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/decoupling-representation-and-classifier-for#ran","syntology_url":"https://syntology.ai/paper/1910.09217","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.09217"}},"official":{"repos":["facebookresearch/classifier-balancing"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-network-classification-by-scattering-and","slug":"deep-network-classification-by-scattering-and","title":"Deep Network Classification by Scattering and Homotopy Dictionary Learning","date":"2019-10-08","arxiv_id":"1910.03561","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":0,"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; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deep-network-classification-by-scattering-and#ran","syntology_url":"https://syntology.ai/paper/1910.03561","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.03561"}},"official":{"repos":["j-zarka/SparseScatNet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-multisensory-scene-inference","slug":"neural-multisensory-scene-inference","title":"Neural Multisensory Scene Inference","date":"2019-10-06","arxiv_id":"1910.02344","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neural-multisensory-scene-inference#ran","syntology_url":"https://syntology.ai/paper/1910.02344","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.02344"}},"official":{"repos":["lim0606/pytorch-generative-multisensory-network"],"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":["listed","official"]}}},{"url":"/paper/scalable-object-oriented-sequential-1","slug":"scalable-object-oriented-sequential-1","title":"SCALOR: Generative World Models with Scalable Object Representations","date":"2019-10-06","arxiv_id":"1910.02384","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/scalable-object-oriented-sequential-1#ran","syntology_url":"https://syntology.ai/paper/1910.02384","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.02384"}},"official":null}},{"url":"/paper/improving-sample-efficiency-in-model-free-1","slug":"improving-sample-efficiency-in-model-free-1","title":"Improving Sample Efficiency in Model-Free Reinforcement Learning from Images","date":"2019-10-02","arxiv_id":"1910.01741","repositories_listed":4,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":2,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"6 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/improving-sample-efficiency-in-model-free-1#ran","syntology_url":"https://syntology.ai/paper/1910.01741","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.01741"}},"official":{"repos":["denisyarats/pytorch_sac_ae"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/predicting-materials-properties-without","slug":"predicting-materials-properties-without","title":"Predicting materials properties without crystal structure: Deep representation learning from stoichiometry","date":"2019-10-01","arxiv_id":"1910.00617","repositories_listed":3,"syntology":{"n":12,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":6,"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) · 6 unverified","sample_list":"/paper/predicting-materials-properties-without#ran","syntology_url":"https://syntology.ai/paper/1910.00617","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.00617"}},"official":{"repos":["CompRhys/roost"],"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/the-visual-task-adaptation-benchmark","slug":"the-visual-task-adaptation-benchmark","title":"A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark","date":"2019-10-01","arxiv_id":"1910.04867","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/the-visual-task-adaptation-benchmark#ran","syntology_url":"https://syntology.ai/paper/1910.04867","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.04867"}},"official":{"repos":["google-research/task_adaptation"],"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/rgbd-gan-unsupervised-3d-representation-1","slug":"rgbd-gan-unsupervised-3d-representation-1","title":"RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis","date":"2019-09-27","arxiv_id":"1909.12573","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rgbd-gan-unsupervised-3d-representation-1#ran","syntology_url":"https://syntology.ai/paper/1909.12573","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.12573"}},"official":null}},{"url":"/paper/uniter-learning-universal-image-text-1","slug":"uniter-learning-universal-image-text-1","title":"UNITER: UNiversal Image-TExt Representation Learning","date":"2019-09-25","arxiv_id":"1909.11740","repositories_listed":7,"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/uniter-learning-universal-image-text-1#ran","syntology_url":"https://syntology.ai/paper/1909.11740","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.11740"}},"official":{"repos":["ChenRocks/UNITER"],"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/revealing-the-importance-of-semantic","slug":"revealing-the-importance-of-semantic","title":"Revealing the Importance of Semantic Retrieval for Machine Reading at Scale","date":"2019-09-17","arxiv_id":"1909.08041","repositories_listed":2,"syntology":{"n":17,"n_ran":15,"n_constructed":0,"n_ran_checked":13,"n_instrument":2,"n_unverified":2,"n_honours":4,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 4 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/revealing-the-importance-of-semantic#ran","syntology_url":"https://syntology.ai/paper/1909.08041","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.08041"}},"official":{"repos":["easonnie/semanticRetrievalMRS"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["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/video-representation-learning-by-dense","slug":"video-representation-learning-by-dense","title":"Video Representation Learning by Dense Predictive Coding","date":"2019-09-10","arxiv_id":"1909.04656","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"9 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/video-representation-learning-by-dense#ran","syntology_url":"https://syntology.ai/paper/1909.04656","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.04656"}},"official":{"repos":["TengdaHan/DPC"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-based-reasoning-over-heterogeneous","slug":"graph-based-reasoning-over-heterogeneous","title":"Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering","date":"2019-09-09","arxiv_id":"1909.05311","repositories_listed":1,"syntology":{"n":17,"n_ran":12,"n_constructed":0,"n_ran_checked":7,"n_instrument":5,"n_unverified":5,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 5 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/graph-based-reasoning-over-heterogeneous#ran","syntology_url":"https://syntology.ai/paper/1909.05311","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.05311"}},"official":{"repos":["DecstionBack/AAAI_2020_CommonsenseQA"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/prediction-consistency-curvature","slug":"prediction-consistency-curvature","title":"Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control","date":"2019-09-04","arxiv_id":"1909.01506","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":8,"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, 1 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/prediction-consistency-curvature#ran","syntology_url":"https://syntology.ai/paper/1909.01506","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.01506"}},"official":null}},{"url":"/paper/bottom-up-higher-resolution-networks-for","slug":"bottom-up-higher-resolution-networks-for","title":"HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation","date":"2019-08-27","arxiv_id":"1908.10357","repositories_listed":19,"syntology":{"n":27,"n_ran":17,"n_constructed":0,"n_ran_checked":13,"n_instrument":4,"n_unverified":10,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":2,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 4 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/bottom-up-higher-resolution-networks-for#ran","syntology_url":"https://syntology.ai/paper/1908.10357","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.10357"}},"official":{"repos":["HRNet/Higher-HRNet-Human-Pose-Estimation"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/variational-graph-recurrent-neural-networks","slug":"variational-graph-recurrent-neural-networks","title":"Variational Graph Recurrent Neural Networks","date":"2019-08-26","arxiv_id":"1908.09710","repositories_listed":2,"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/variational-graph-recurrent-neural-networks#ran","syntology_url":"https://syntology.ai/paper/1908.09710","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.09710"}},"official":{"repos":["VGraphRNN/VGRNN"],"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/tabnet-attentive-interpretable-tabular","slug":"tabnet-attentive-interpretable-tabular","title":"TabNet: Attentive Interpretable Tabular Learning","date":"2019-08-20","arxiv_id":"1908.07442","repositories_listed":19,"syntology":{"n":17,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":1,"phrase":"13 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; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/tabnet-attentive-interpretable-tabular#ran","syntology_url":"https://syntology.ai/paper/1908.07442","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.07442"}},"official":{"repos":["google-research/google-research"],"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/190807919","slug":"190807919","title":"Deep High-Resolution Representation Learning for Visual Recognition","date":"2019-08-20","arxiv_id":"1908.07919","repositories_listed":42,"syntology":{"n":34,"n_ran":21,"n_constructed":0,"n_ran_checked":19,"n_instrument":2,"n_unverified":13,"n_honours":0,"n_violates":1,"n_no_contract":18,"n_pointer_only":21,"phrase":"21 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 1 violated, 18 with no contract checked; 2 where Syntology's instrument failed) · 13 unverified","sample_list":"/paper/190807919#ran","syntology_url":"https://syntology.ai/paper/1908.07919","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.07919"}},"official":null}},{"url":"/paper/n2dnot-too-deep-clustering-via-clustering-the","slug":"n2dnot-too-deep-clustering-via-clustering-the","title":"N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding","date":"2019-08-16","arxiv_id":"1908.05968","repositories_listed":5,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/n2dnot-too-deep-clustering-via-clustering-the#ran","syntology_url":"https://syntology.ai/paper/1908.05968","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.05968"}},"official":{"repos":["rymc/n2d"],"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/taper-time-aware-patient-ehr-representation","slug":"taper-time-aware-patient-ehr-representation","title":"TAPER: Time-Aware Patient EHR Representation","date":"2019-08-11","arxiv_id":"1908.03971","repositories_listed":2,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/taper-time-aware-patient-ehr-representation#ran","syntology_url":"https://syntology.ai/paper/1908.03971","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.03971"}},"official":{"repos":["sajaddarabi/TAPER","sajaddarabi/TAPER-EHR"],"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/symmetric-graph-convolutional-autoencoder-for","slug":"symmetric-graph-convolutional-autoencoder-for","title":"Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning","date":"2019-08-07","arxiv_id":"1908.02441","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/symmetric-graph-convolutional-autoencoder-for#ran","syntology_url":"https://syntology.ai/paper/1908.02441","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.02441"}},"official":null}},{"url":"/paper/predicting-dynamic-embedding-trajectory-in","slug":"predicting-dynamic-embedding-trajectory-in","title":"Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks","date":"2019-08-03","arxiv_id":"1908.01207","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/predicting-dynamic-embedding-trajectory-in#ran","syntology_url":"https://syntology.ai/paper/1908.01207","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.01207"}},"official":null}},{"url":"/paper/learning-lightweight-lane-detection-cnns-by","slug":"learning-lightweight-lane-detection-cnns-by","title":"Learning Lightweight Lane Detection CNNs by Self Attention Distillation","date":"2019-08-02","arxiv_id":"1908.00821","repositories_listed":2,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-lightweight-lane-detection-cnns-by#ran","syntology_url":"https://syntology.ai/paper/1908.00821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.00821"}},"official":{"repos":["cardwing/Codes-for-Lane-Detection"],"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":["listed","official"]}}},{"url":"/paper/self-supervised-domain-adaptation-for","slug":"self-supervised-domain-adaptation-for","title":"Self-supervised Domain Adaptation for Computer Vision Tasks","date":"2019-07-25","arxiv_id":"1907.10915","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/self-supervised-domain-adaptation-for#ran","syntology_url":"https://syntology.ai/paper/1907.10915","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.10915"}},"official":{"repos":["Jiaolong/self-supervised-da"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/large-scale-adversarial-representation","slug":"large-scale-adversarial-representation","title":"Large Scale Adversarial Representation Learning","date":"2019-07-04","arxiv_id":"1907.02544","repositories_listed":4,"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/large-scale-adversarial-representation#ran","syntology_url":"https://syntology.ai/paper/1907.02544","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.02544"}},"official":null}},{"url":"/paper/few-shot-representation-learning-for-out-of","slug":"few-shot-representation-learning-for-out-of","title":"Few-Shot Representation Learning for Out-Of-Vocabulary Words","date":"2019-07-01","arxiv_id":"1907.00505","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/few-shot-representation-learning-for-out-of#ran","syntology_url":"https://syntology.ai/paper/1907.00505","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.00505"}},"official":{"repos":["acbull/HiCE"],"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/stochastic-latent-actor-critic-deep","slug":"stochastic-latent-actor-critic-deep","title":"Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model","date":"2019-07-01","arxiv_id":"1907.00953","repositories_listed":9,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/stochastic-latent-actor-critic-deep#ran","syntology_url":"https://syntology.ai/paper/1907.00953","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.00953"}},"official":null}},{"url":"/paper/unsupervised-state-representation-learning-in","slug":"unsupervised-state-representation-learning-in","title":"Unsupervised State Representation Learning in Atari","date":"2019-06-19","arxiv_id":"1906.08226","repositories_listed":7,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unsupervised-state-representation-learning-in#ran","syntology_url":"https://syntology.ai/paper/1906.08226","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.08226"}},"official":{"repos":["mila-iqia/atari-representation-learning"],"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/evaluating-protein-transfer-learning-with","slug":"evaluating-protein-transfer-learning-with","title":"Evaluating Protein Transfer Learning with TAPE","date":"2019-06-19","arxiv_id":"1906.08230","repositories_listed":5,"syntology":{"n":18,"n_ran":16,"n_constructed":0,"n_ran_checked":12,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"16 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; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/evaluating-protein-transfer-learning-with#ran","syntology_url":"https://syntology.ai/paper/1906.08230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.08230"}},"official":{"repos":["songlab-cal/tape"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/vgraph-a-generative-model-for-joint-community","slug":"vgraph-a-generative-model-for-joint-community","title":"vGraph: A Generative Model for Joint Community Detection and Node Representation Learning","date":"2019-06-18","arxiv_id":"1906.07159","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/vgraph-a-generative-model-for-joint-community#ran","syntology_url":"https://syntology.ai/paper/1906.07159","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.07159"}},"official":null}},{"url":"/paper/probabilistic-forecasting-with-temporal","slug":"probabilistic-forecasting-with-temporal","title":"Probabilistic Forecasting with Temporal Convolutional Neural Network","date":"2019-06-11","arxiv_id":"1906.04397","repositories_listed":5,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/probabilistic-forecasting-with-temporal#ran","syntology_url":"https://syntology.ai/paper/1906.04397","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.04397"}},"official":{"repos":["oneday88/deepTCN"],"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/data-to-text-generation-with-entity-modeling","slug":"data-to-text-generation-with-entity-modeling","title":"Data-to-text Generation with Entity Modeling","date":"2019-06-07","arxiv_id":"1906.03221","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/data-to-text-generation-with-entity-modeling#ran","syntology_url":"https://syntology.ai/paper/1906.03221","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.03221"}},"official":{"repos":["ratishsp/data2text-entity-py"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-transfer-of-inductive-bias-from","slug":"on-the-transfer-of-inductive-bias-from","title":"On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset","date":"2019-06-07","arxiv_id":"1906.03292","repositories_listed":4,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-the-transfer-of-inductive-bias-from#ran","syntology_url":"https://syntology.ai/paper/1906.03292","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.03292"}},"official":{"repos":["rr-learning/disentanglement_dataset"],"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/190600910","slug":"190600910","title":"Learning Representations by Maximizing Mutual Information Across Views","date":"2019-06-03","arxiv_id":"1906.00910","repositories_listed":3,"syntology":{"n":11,"n_ran":8,"n_constructed":5,"n_ran_checked":7,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"phrase":"8 ran (of which 5 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/190600910#ran","syntology_url":"https://syntology.ai/paper/1906.00910","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.00910"}},"official":null}},{"url":"/paper/190600346","slug":"190600346","title":"Pre-training of Graph Augmented Transformers for Medication Recommendation","date":"2019-06-02","arxiv_id":"1906.00346","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/190600346#ran","syntology_url":"https://syntology.ai/paper/1906.00346","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.00346"}},"official":{"repos":["jshang123/G-Bert"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/semantics-aligned-representation-learning-for","slug":"semantics-aligned-representation-learning-for","title":"Semantics-Aligned Representation Learning for Person Re-identification","date":"2019-05-30","arxiv_id":"1905.13143","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"8 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; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/semantics-aligned-representation-learning-for#ran","syntology_url":"https://syntology.ai/paper/1905.13143","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.13143"}},"official":{"repos":["microsoft/Semantics-Aligned-Representation-Learning-for-Person-Re-identification"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/pre-training-graph-neural-networks","slug":"pre-training-graph-neural-networks","title":"Strategies for Pre-training Graph Neural Networks","date":"2019-05-29","arxiv_id":"1905.12265","repositories_listed":11,"syntology":{"n":14,"n_ran":13,"n_constructed":1,"n_ran_checked":9,"n_instrument":4,"n_unverified":1,"n_honours":3,"n_violates":0,"n_no_contract":6,"n_pointer_only":5,"phrase":"13 ran (of which 1 constructed an object rather than computing a result; 9 with no instrument failure: 3 honoured, 0 violated, 6 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pre-training-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/1905.12265","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.12265"}},"official":{"repos":["snap-stanford/pretrain-gnns"],"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/greedy-infomax-for-biologically-plausible","slug":"greedy-infomax-for-biologically-plausible","title":"Putting An End to End-to-End: Gradient-Isolated Learning of Representations","date":"2019-05-28","arxiv_id":"1905.11786","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/greedy-infomax-for-biologically-plausible#ran","syntology_url":"https://syntology.ai/paper/1905.11786","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.11786"}},"official":{"repos":["loeweX/Greedy_InfoMax"],"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/combating-label-noise-in-deep-learning-using","slug":"combating-label-noise-in-deep-learning-using","title":"Combating Label Noise in Deep Learning Using Abstention","date":"2019-05-27","arxiv_id":"1905.10964","repositories_listed":2,"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/combating-label-noise-in-deep-learning-using#ran","syntology_url":"https://syntology.ai/paper/1905.10964","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.10964"}},"official":{"repos":["thulas/dac-label-noise"],"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/on-variational-bounds-of-mutual-information","slug":"on-variational-bounds-of-mutual-information","title":"On Variational Bounds of Mutual Information","date":"2019-05-16","arxiv_id":"1905.06922","repositories_listed":3,"syntology":{"n":21,"n_ran":17,"n_constructed":1,"n_ran_checked":16,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":16,"n_pointer_only":1,"phrase":"17 ran (of which 1 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/on-variational-bounds-of-mutual-information#ran","syntology_url":"https://syntology.ai/paper/1905.06922","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.06922"}},"official":null}},{"url":"/paper/gmnn-graph-markov-neural-networks","slug":"gmnn-graph-markov-neural-networks","title":"GMNN: Graph Markov Neural Networks","date":"2019-05-15","arxiv_id":"1905.06214","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/gmnn-graph-markov-neural-networks#ran","syntology_url":"https://syntology.ai/paper/1905.06214","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.06214"}},"official":null}},{"url":"/paper/graph-u-nets","slug":"graph-u-nets","title":"Graph U-Nets","date":"2019-05-11","arxiv_id":"1905.05178","repositories_listed":3,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":2,"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; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/graph-u-nets#ran","syntology_url":"https://syntology.ai/paper/1905.05178","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.05178"}},"official":{"repos":["HongyangGao/gunet"],"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/190503670","slug":"190503670","title":"S4L: Self-Supervised Semi-Supervised Learning","date":"2019-05-09","arxiv_id":"1905.03670","repositories_listed":1,"syntology":{"n":19,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":19,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/190503670#ran","syntology_url":"https://syntology.ai/paper/1905.03670","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.03670"}},"official":null}},{"url":"/paper/190501669","slug":"190501669","title":"Representation Learning for Attributed Multiplex Heterogeneous Network","date":"2019-05-05","arxiv_id":"1905.01669","repositories_listed":4,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/190501669#ran","syntology_url":"https://syntology.ai/paper/1905.01669","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.01669"}},"official":{"repos":["cenyk1230/GATNE"],"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/adversarial-balancing-based-representation","slug":"adversarial-balancing-based-representation","title":"Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data","date":"2019-04-30","arxiv_id":"1904.13335","repositories_listed":2,"syntology":{"n":23,"n_ran":18,"n_constructed":0,"n_ran_checked":17,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":2,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/adversarial-balancing-based-representation#ran","syntology_url":"https://syntology.ai/paper/1904.13335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.13335"}},"official":{"repos":["octeufer/Adversarial-Balancing-based-representation-learning-for-Causal-Effect-Inference"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/switchable-whitening-for-deep-representation","slug":"switchable-whitening-for-deep-representation","title":"Switchable Whitening for Deep Representation Learning","date":"2019-04-22","arxiv_id":"1904.09739","repositories_listed":1,"syntology":{"n":10,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":8,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/switchable-whitening-for-deep-representation#ran","syntology_url":"https://syntology.ai/paper/1904.09739","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.09739"}},"official":{"repos":["XingangPan/Switchable-Whitening"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/temporal-cycle-consistency-learning","slug":"temporal-cycle-consistency-learning","title":"Temporal Cycle-Consistency Learning","date":"2019-04-16","arxiv_id":"1904.07846","repositories_listed":2,"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/temporal-cycle-consistency-learning#ran","syntology_url":"https://syntology.ai/paper/1904.07846","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.07846"}},"official":null}},{"url":"/paper/high-resolution-representations-for-labeling","slug":"high-resolution-representations-for-labeling","title":"High-Resolution Representations for Labeling Pixels and Regions","date":"2019-04-09","arxiv_id":"1904.04514","repositories_listed":39,"syntology":{"n":18,"n_ran":14,"n_constructed":0,"n_ran_checked":11,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":11,"phrase":"14 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; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/high-resolution-representations-for-labeling#ran","syntology_url":"https://syntology.ai/paper/1904.04514","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.04514"}},"official":{"repos":["leoxiaobin/deep-high-resolution-net.pytorch"],"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/an-unsupervised-autoregressive-model-for","slug":"an-unsupervised-autoregressive-model-for","title":"An Unsupervised Autoregressive Model for Speech Representation Learning","date":"2019-04-05","arxiv_id":"1904.03240","repositories_listed":4,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"4 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/an-unsupervised-autoregressive-model-for#ran","syntology_url":"https://syntology.ai/paper/1904.03240","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.03240"}},"official":null}},{"url":"/paper/videobert-a-joint-model-for-video-and","slug":"videobert-a-joint-model-for-video-and","title":"VideoBERT: A Joint Model for Video and Language Representation Learning","date":"2019-04-03","arxiv_id":"1904.01766","repositories_listed":3,"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/videobert-a-joint-model-for-video-and#ran","syntology_url":"https://syntology.ai/paper/1904.01766","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.01766"}},"official":null}},{"url":"/paper/unsupervised-continual-learning-and-self","slug":"unsupervised-continual-learning-and-self","title":"Unsupervised Progressive Learning and the STAM Architecture","date":"2019-04-03","arxiv_id":"1904.02021","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/unsupervised-continual-learning-and-self#ran","syntology_url":"https://syntology.ai/paper/1904.02021","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.02021"}},"official":{"repos":["CameronTaylorFL/stam"],"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/symmetry-based-disentangled-representation","slug":"symmetry-based-disentangled-representation","title":"Symmetry-Based Disentangled Representation Learning requires Interaction with Environments","date":"2019-03-30","arxiv_id":"1904.00243","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/symmetry-based-disentangled-representation#ran","syntology_url":"https://syntology.ai/paper/1904.00243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.00243"}},"official":{"repos":["Caselles/NeurIPS19-SBDRL"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/factorised-representation-learning-in-cardiac","slug":"factorised-representation-learning-in-cardiac","title":"Disentangled Representation Learning in Cardiac Image Analysis","date":"2019-03-22","arxiv_id":"1903.09467","repositories_listed":4,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/factorised-representation-learning-in-cardiac#ran","syntology_url":"https://syntology.ai/paper/1903.09467","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.09467"}},"official":{"repos":["agis85/anatomy_modality_decomposition"],"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/minatar-an-atari-inspired-testbed-for-more","slug":"minatar-an-atari-inspired-testbed-for-more","title":"MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments","date":"2019-03-07","arxiv_id":"1903.03176","repositories_listed":3,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/minatar-an-atari-inspired-testbed-for-more#ran","syntology_url":"https://syntology.ai/paper/1903.03176","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.03176"}},"official":{"repos":["kenjyoung/MinAtar"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/multi-object-representation-learning-with","slug":"multi-object-representation-learning-with","title":"Multi-Object Representation Learning with Iterative Variational Inference","date":"2019-03-01","arxiv_id":"1903.00450","repositories_listed":6,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/multi-object-representation-learning-with#ran","syntology_url":"https://syntology.ai/paper/1903.00450","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.00450"}},"official":{"repos":["deepmind/deepmind-research"],"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/disentangled-representation-learning-for-3d","slug":"disentangled-representation-learning-for-3d","title":"Disentangled Representation Learning for 3D Face Shape","date":"2019-02-26","arxiv_id":"1902.09887","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/disentangled-representation-learning-for-3d#ran","syntology_url":"https://syntology.ai/paper/1902.09887","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.09887"}},"official":{"repos":["zihangJiang/DR-Learning-for-3D-Face"],"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/evolvegcn-evolving-graph-convolutional","slug":"evolvegcn-evolving-graph-convolutional","title":"EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs","date":"2019-02-26","arxiv_id":"1902.10191","repositories_listed":10,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/evolvegcn-evolving-graph-convolutional#ran","syntology_url":"https://syntology.ai/paper/1902.10191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.10191"}},"official":{"repos":["IBM/AMLSim","IBM/EvolveGCN"],"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":["listed","official"]}}},{"url":"/paper/deep-high-resolution-representation-learning","slug":"deep-high-resolution-representation-learning","title":"Deep High-Resolution Representation Learning for Human Pose Estimation","date":"2019-02-25","arxiv_id":"1902.09212","repositories_listed":39,"syntology":{"n":25,"n_ran":16,"n_constructed":0,"n_ran_checked":8,"n_instrument":8,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":2,"phrase":"16 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; 8 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/deep-high-resolution-representation-learning#ran","syntology_url":"https://syntology.ai/paper/1902.09212","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.09212"}},"official":{"repos":["leoxiaobin/deep-high-resolution-net.pytorch","Microsoft/human-pose-estimation.pytorch"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/learning-representations-of-irregular","slug":"learning-representations-of-irregular","title":"Learning representations of irregular particle-detector geometry with distance-weighted graph networks","date":"2019-02-21","arxiv_id":"1902.07987","repositories_listed":3,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-representations-of-irregular#ran","syntology_url":"https://syntology.ai/paper/1902.07987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.07987"}},"official":{"repos":["jkiesele/caloGraphNN"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed"]}}},{"url":"/paper/measuring-compositionality-in-representation","slug":"measuring-compositionality-in-representation","title":"Measuring Compositionality in Representation Learning","date":"2019-02-19","arxiv_id":"1902.07181","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/measuring-compositionality-in-representation#ran","syntology_url":"https://syntology.ai/paper/1902.07181","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.07181"}},"official":{"repos":["jacobandreas/tre"],"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/block-bilinear-superdiagonal-fusion-for","slug":"block-bilinear-superdiagonal-fusion-for","title":"BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection","date":"2019-01-31","arxiv_id":"1902.00038","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/block-bilinear-superdiagonal-fusion-for#ran","syntology_url":"https://syntology.ai/paper/1902.00038","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.00038"}},"official":{"repos":["Cadene/block.bootstrap.pytorch"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-scalable-representation-learning","slug":"unsupervised-scalable-representation-learning","title":"Unsupervised Scalable Representation Learning for Multivariate Time Series","date":"2019-01-30","arxiv_id":"1901.10738","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/unsupervised-scalable-representation-learning#ran","syntology_url":"https://syntology.ai/paper/1901.10738","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.10738"}},"official":{"repos":["White-Link/UnsupervisedScalableRepresentationLearningTimeSeries"],"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/biobert-a-pre-trained-biomedical-language","slug":"biobert-a-pre-trained-biomedical-language","title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","date":"2019-01-25","arxiv_id":"1901.08746","repositories_listed":19,"syntology":{"n":25,"n_ran":15,"n_constructed":0,"n_ran_checked":12,"n_instrument":3,"n_unverified":10,"n_honours":1,"n_violates":0,"n_no_contract":11,"n_pointer_only":3,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 3 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/biobert-a-pre-trained-biomedical-language#ran","syntology_url":"https://syntology.ai/paper/1901.08746","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.08746"}},"official":{"repos":["dmis-lab/biobert","naver/biobert-pretrained"],"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/revisiting-self-supervised-visual","slug":"revisiting-self-supervised-visual","title":"Revisiting Self-Supervised Visual Representation Learning","date":"2019-01-25","arxiv_id":"1901.09005","repositories_listed":6,"syntology":{"n":15,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":7,"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) · 6 unverified","sample_list":"/paper/revisiting-self-supervised-visual#ran","syntology_url":"https://syntology.ai/paper/1901.09005","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.09005"}},"official":{"repos":["google/revisiting-self-supervised"],"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":["listed","official"]}}},{"url":"/paper/loss-landscapes-of-regularized-linear","slug":"loss-landscapes-of-regularized-linear","title":"Loss Landscapes of Regularized Linear Autoencoders","date":"2019-01-23","arxiv_id":"1901.08168","repositories_listed":2,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/loss-landscapes-of-regularized-linear#ran","syntology_url":"https://syntology.ai/paper/1901.08168","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.08168"}},"official":{"repos":["danielkunin/Regularized-Linear-Autoencoders"],"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/challenging-common-assumptions-in-the","slug":"challenging-common-assumptions-in-the","title":"Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations","date":"2018-11-29","arxiv_id":"1811.12359","repositories_listed":8,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"3 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/challenging-common-assumptions-in-the#ran","syntology_url":"https://syntology.ai/paper/1811.12359","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.12359"}},"official":{"repos":["google-research/disentanglement_lib"],"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":["listed","official"]}}},{"url":"/paper/self-supervised-generative-adversarial","slug":"self-supervised-generative-adversarial","title":"Self-Supervised GANs via Auxiliary Rotation Loss","date":"2018-11-27","arxiv_id":"1811.11212","repositories_listed":4,"syntology":{"n":11,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":8,"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) · 8 unverified","sample_list":"/paper/self-supervised-generative-adversarial#ran","syntology_url":"https://syntology.ai/paper/1811.11212","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.11212"}},"official":{"repos":["google/compare_gan"],"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/grasp2vec-learning-object-representations","slug":"grasp2vec-learning-object-representations","title":"Grasp2Vec: Learning Object Representations from Self-Supervised Grasping","date":"2018-11-16","arxiv_id":"1811.06964","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 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) · 4 unverified","sample_list":"/paper/grasp2vec-learning-object-representations#ran","syntology_url":"https://syntology.ai/paper/1811.06964","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.06964"}},"official":null}},{"url":"/paper/multi-task-graph-autoencoders","slug":"multi-task-graph-autoencoders","title":"Multi-Task Graph Autoencoders","date":"2018-11-07","arxiv_id":"1811.02798","repositories_listed":1,"syntology":{"n":9,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":6,"n_honours":1,"n_violates":1,"n_no_contract":1,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/multi-task-graph-autoencoders#ran","syntology_url":"https://syntology.ai/paper/1811.02798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.02798"}},"official":{"repos":["vuptran/graph-representation-learning"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-symbolic-vqa-disentangling-reasoning","slug":"neural-symbolic-vqa-disentangling-reasoning","title":"Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding","date":"2018-10-04","arxiv_id":"1810.02338","repositories_listed":2,"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":2,"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/neural-symbolic-vqa-disentangling-reasoning#ran","syntology_url":"https://syntology.ai/paper/1810.02338","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.02338"}},"official":null}},{"url":"/paper/how-powerful-are-graph-neural-networks","slug":"how-powerful-are-graph-neural-networks","title":"How Powerful are Graph Neural Networks?","date":"2018-10-01","arxiv_id":"1810.00826","repositories_listed":19,"syntology":{"n":10,"n_ran":6,"n_constructed":2,"n_ran_checked":6,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":5,"phrase":"6 ran (of which 2 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) · 4 unverified","sample_list":"/paper/how-powerful-are-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/1810.00826","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.00826"}},"official":{"repos":["weihua916/powerful-gnns"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/morpho-mnist-quantitative-assessment-and","slug":"morpho-mnist-quantitative-assessment-and","title":"Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning","date":"2018-09-27","arxiv_id":"1809.10780","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/morpho-mnist-quantitative-assessment-and#ran","syntology_url":"https://syntology.ai/paper/1809.10780","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.10780"}},"official":{"repos":["dccastro/Morpho-MNIST"],"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/hypergraph-neural-networks","slug":"hypergraph-neural-networks","title":"Hypergraph Neural Networks","date":"2018-09-25","arxiv_id":"1809.09401","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":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) · 1 unverified","sample_list":"/paper/hypergraph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/1809.09401","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.09401"}},"official":null}},{"url":"/paper/open-domain-question-answering-using-early","slug":"open-domain-question-answering-using-early","title":"Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text","date":"2018-09-04","arxiv_id":"1809.00782","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/open-domain-question-answering-using-early#ran","syntology_url":"https://syntology.ai/paper/1809.00782","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.00782"}},"official":{"repos":["OceanskySun/GraftNet"],"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/adversarial-attacks-on-node-embeddings","slug":"adversarial-attacks-on-node-embeddings","title":"Adversarial Attacks on Node Embeddings via Graph Poisoning","date":"2018-09-04","arxiv_id":"1809.01093","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/adversarial-attacks-on-node-embeddings#ran","syntology_url":"https://syntology.ai/paper/1809.01093","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.01093"}},"official":null}},{"url":"/paper/look-across-elapse-disentangled","slug":"look-across-elapse-disentangled","title":"Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition","date":"2018-09-02","arxiv_id":"1809.00338","repositories_listed":1,"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/look-across-elapse-disentangled#ran","syntology_url":"https://syntology.ai/paper/1809.00338","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.00338"}},"official":{"repos":["ZhaoJ9014/High_Performance_Face_Recognition"],"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/learning-deep-representations-by-mutual","slug":"learning-deep-representations-by-mutual","title":"Learning deep representations by mutual information estimation and maximization","date":"2018-08-20","arxiv_id":"1808.06670","repositories_listed":8,"syntology":{"n":6,"n_ran":5,"n_constructed":2,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-deep-representations-by-mutual#ran","syntology_url":"https://syntology.ai/paper/1808.06670","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.06670"}},"official":{"repos":["rdevon/DIM"],"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/disentangled-representation-learning-for-non","slug":"disentangled-representation-learning-for-non","title":"Disentangled Representation Learning for Non-Parallel Text Style Transfer","date":"2018-08-13","arxiv_id":"1808.04339","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/disentangled-representation-learning-for-non#ran","syntology_url":"https://syntology.ai/paper/1808.04339","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.04339"}},"official":{"repos":["vineetjohn/linguistic-style-transfer"],"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/simultaneous-edge-alignment-and-learning","slug":"simultaneous-edge-alignment-and-learning","title":"Simultaneous Edge Alignment and Learning","date":"2018-08-06","arxiv_id":"1808.01992","repositories_listed":3,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/simultaneous-edge-alignment-and-learning#ran","syntology_url":"https://syntology.ai/paper/1808.01992","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.01992"}},"official":{"repos":["Chrisding/seal","Chrisding/sbd-preprocess"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/instance-level-human-parsing-via-part","slug":"instance-level-human-parsing-via-part","title":"Instance-level Human Parsing via Part Grouping Network","date":"2018-08-01","arxiv_id":"1808.00157","repositories_listed":1,"syntology":{"n":14,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":8,"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) · 8 unverified","sample_list":"/paper/instance-level-human-parsing-via-part#ran","syntology_url":"https://syntology.ai/paper/1808.00157","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.00157"}},"official":{"repos":["Engineering-Course/CIHP_PGN"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/discovering-physical-concepts-with-neural","slug":"discovering-physical-concepts-with-neural","title":"Discovering physical concepts with neural networks","date":"2018-07-26","arxiv_id":"1807.10300","repositories_listed":7,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":3,"n_no_contract":2,"n_pointer_only":3,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 3 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/discovering-physical-concepts-with-neural#ran","syntology_url":"https://syntology.ai/paper/1807.10300","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.10300"}},"official":{"repos":["eth-nn-physics/nn_physical_concepts"],"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":["listed","official"]}}},{"url":"/paper/learning-plannable-representations-with","slug":"learning-plannable-representations-with","title":"Learning Plannable Representations with Causal InfoGAN","date":"2018-07-24","arxiv_id":"1807.09341","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-plannable-representations-with#ran","syntology_url":"https://syntology.ai/paper/1807.09341","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.09341"}},"official":{"repos":["thanard/causal-infogan"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptive-neural-trees","slug":"adaptive-neural-trees","title":"Adaptive Neural Trees","date":"2018-07-17","arxiv_id":"1807.06699","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/adaptive-neural-trees#ran","syntology_url":"https://syntology.ai/paper/1807.06699","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.06699"}},"official":{"repos":["rtanno21609/AdaptiveNeuralTrees"],"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/deepinf-social-influence-prediction-with-deep","slug":"deepinf-social-influence-prediction-with-deep","title":"DeepInf: Social Influence Prediction with Deep Learning","date":"2018-07-15","arxiv_id":"1807.05560","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/deepinf-social-influence-prediction-with-deep#ran","syntology_url":"https://syntology.ai/paper/1807.05560","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.05560"}},"official":{"repos":["xptree/DeepInf"],"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/representation-learning-with-contrastive","slug":"representation-learning-with-contrastive","title":"Representation Learning with Contrastive Predictive Coding","date":"2018-07-10","arxiv_id":"1807.03748","repositories_listed":28,"syntology":{"n":45,"n_ran":35,"n_constructed":21,"n_ran_checked":30,"n_instrument":5,"n_unverified":10,"n_honours":1,"n_violates":0,"n_no_contract":29,"n_pointer_only":22,"phrase":"35 ran (of which 21 constructed an object rather than computing a result; 30 with no instrument failure: 1 honoured, 0 violated, 29 with no contract checked; 5 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/representation-learning-with-contrastive#ran","syntology_url":"https://syntology.ai/paper/1807.03748","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.03748"}},"official":null}},{"url":"/paper/variational-wasserstein-clustering","slug":"variational-wasserstein-clustering","title":"Variational Wasserstein Clustering","date":"2018-06-23","arxiv_id":"1806.09045","repositories_listed":2,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":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) · 2 unverified","sample_list":"/paper/variational-wasserstein-clustering#ran","syntology_url":"https://syntology.ai/paper/1806.09045","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.09045"}},"official":{"repos":["icemiliang/vot"],"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/hierarchical-graph-representation-learning","slug":"hierarchical-graph-representation-learning","title":"Hierarchical Graph Representation Learning with Differentiable Pooling","date":"2018-06-22","arxiv_id":"1806.08804","repositories_listed":14,"syntology":{"n":20,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":10,"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) · 10 unverified","sample_list":"/paper/hierarchical-graph-representation-learning#ran","syntology_url":"https://syntology.ai/paper/1806.08804","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.08804"}},"official":null}},{"url":"/paper/learning-dynamics-of-linear-denoising","slug":"learning-dynamics-of-linear-denoising","title":"Learning Dynamics of Linear Denoising Autoencoders","date":"2018-06-14","arxiv_id":"1806.05413","repositories_listed":1,"syntology":{"n":19,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/learning-dynamics-of-linear-denoising#ran","syntology_url":"https://syntology.ai/paper/1806.05413","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.05413"}},"official":{"repos":["arnupretorius/lindaedynamics_icml2018"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-representations-of-ultrahigh","slug":"learning-representations-of-ultrahigh","title":"Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection","date":"2018-06-13","arxiv_id":"1806.04808","repositories_listed":3,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-representations-of-ultrahigh#ran","syntology_url":"https://syntology.ai/paper/1806.04808","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.04808"}},"official":null}},{"url":"/paper/representation-learning-on-graphs-with","slug":"representation-learning-on-graphs-with","title":"Representation Learning on Graphs with Jumping Knowledge Networks","date":"2018-06-09","arxiv_id":"1806.03536","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/representation-learning-on-graphs-with#ran","syntology_url":"https://syntology.ai/paper/1806.03536","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.03536"}},"official":null}},{"url":"/paper/som-vae-interpretable-discrete-representation","slug":"som-vae-interpretable-discrete-representation","title":"SOM-VAE: Interpretable Discrete Representation Learning on Time Series","date":"2018-06-06","arxiv_id":"1806.02199","repositories_listed":6,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/som-vae-interpretable-discrete-representation#ran","syntology_url":"https://syntology.ai/paper/1806.02199","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.02199"}},"official":{"repos":["ratschlab/SOM-VAE"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/spectral-inference-networks-unifying-spectral","slug":"spectral-inference-networks-unifying-spectral","title":"Spectral Inference Networks: Unifying Deep and Spectral Learning","date":"2018-06-06","arxiv_id":"1806.02215","repositories_listed":2,"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/spectral-inference-networks-unifying-spectral#ran","syntology_url":"https://syntology.ai/paper/1806.02215","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.02215"}},"official":{"repos":["deepmind/spectral_inference_networks"],"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/invariant-representations-without-adversarial","slug":"invariant-representations-without-adversarial","title":"Invariant Representations without Adversarial Training","date":"2018-05-24","arxiv_id":"1805.09458","repositories_listed":1,"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/invariant-representations-without-adversarial#ran","syntology_url":"https://syntology.ai/paper/1805.09458","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.09458"}},"official":null}},{"url":"/paper/learning-permutations-with-sinkhorn-policy","slug":"learning-permutations-with-sinkhorn-policy","title":"Learning Permutations with Sinkhorn Policy Gradient","date":"2018-05-18","arxiv_id":"1805.07010","repositories_listed":1,"syntology":{"n":10,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":5,"n_honours":1,"n_violates":1,"n_no_contract":3,"n_pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/learning-permutations-with-sinkhorn-policy#ran","syntology_url":"https://syntology.ai/paper/1805.07010","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.07010"}},"official":null}},{"url":"/paper/efficient-end-to-end-learning-for-quantizable","slug":"efficient-end-to-end-learning-for-quantizable","title":"Efficient end-to-end learning for quantizable representations","date":"2018-05-15","arxiv_id":"1805.05809","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/efficient-end-to-end-learning-for-quantizable#ran","syntology_url":"https://syntology.ai/paper/1805.05809","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.05809"}},"official":{"repos":["maestrojeong/Deep-Hash-Table-ICML18"],"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/sopa-bridging-cnns-rnns-and-weighted-finite","slug":"sopa-bridging-cnns-rnns-and-weighted-finite","title":"SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines","date":"2018-05-15","arxiv_id":"1805.06061","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/sopa-bridging-cnns-rnns-and-weighted-finite#ran","syntology_url":"https://syntology.ai/paper/1805.06061","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.06061"}},"official":{"repos":["Noahs-ARK/soft_patterns"],"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":["listed","official"]}}}],"record_sha256":"dd89930fa8bb316da958f118917d7cd7e8bb77a2001d1a9e6fc09594abebff5d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}