{"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/model/papers/ran/5","list_of":"/task/model","task":"model","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":5,"pages_in_order":6,"rows_per_page":100,"rows":[401,500],"of":517,"counts":{"archive_papers_tagged":5434,"with_a_code_link":1733,"where_syntology_ran_a_sample":517,"not_listed_spam_title":0,"listed":5434,"listed_where_code_ran":517,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":424,"every_run_a_failure_of_syntologys_instrument":93,"listed_with_a_run_with_no_instrument_failure":424,"listed_every_run_a_failure_of_syntologys_instrument":93,"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/model/papers/ran/1","prev":"/task/model/papers/ran/4","next":"/task/model/papers/ran/6","papers":[{"url":"/paper/dangers-of-bayesian-model-averaging-under","slug":"dangers-of-bayesian-model-averaging-under","title":"Dangers of Bayesian Model Averaging under Covariate Shift","date":"2021-06-22","arxiv_id":"2106.11905","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dangers-of-bayesian-model-averaging-under#ran","syntology_url":"https://syntology.ai/paper/2106.11905","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.11905"}},"official":{"repos":["izmailovpavel/bnn_covariate_shift"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/s-lime-stabilized-lime-for-model-explanation","slug":"s-lime-stabilized-lime-for-model-explanation","title":"S-LIME: Stabilized-LIME for Model Explanation","date":"2021-06-15","arxiv_id":"2106.07875","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":1,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/s-lime-stabilized-lime-for-model-explanation#ran","syntology_url":"https://syntology.ai/paper/2106.07875","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.07875"}},"official":{"repos":["ZhengzeZhou/slime"],"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/model-selection-for-bayesian-autoencoders","slug":"model-selection-for-bayesian-autoencoders","title":"Model Selection for Bayesian Autoencoders","date":"2021-06-11","arxiv_id":"2106.06245","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/model-selection-for-bayesian-autoencoders#ran","syntology_url":"https://syntology.ai/paper/2106.06245","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06245"}},"official":{"repos":["tranbahien/bae-prior"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/boosting-a-model-zoo-for-multi-task-and","slug":"boosting-a-model-zoo-for-multi-task-and","title":"Model Zoo: A Growing \"Brain\" That Learns Continually","date":"2021-06-06","arxiv_id":"2106.03027","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/boosting-a-model-zoo-for-multi-task-and#ran","syntology_url":"https://syntology.ai/paper/2106.03027","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03027"}},"official":{"repos":["grasp-lyrl/modelzoo_continual","rahul13ramesh/modelzoo_continual"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/retrieval-enhanced-model-for-commonsense","slug":"retrieval-enhanced-model-for-commonsense","title":"Retrieval Enhanced Model for Commonsense Generation","date":"2021-05-24","arxiv_id":"2105.11174","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"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/retrieval-enhanced-model-for-commonsense#ran","syntology_url":"https://syntology.ai/paper/2105.11174","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.11174"}},"official":{"repos":["HanNight/RE-T5"],"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/pathdreamer-a-world-model-for-indoor","slug":"pathdreamer-a-world-model-for-indoor","title":"Pathdreamer: A World Model for Indoor Navigation","date":"2021-05-18","arxiv_id":"2105.08756","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pathdreamer-a-world-model-for-indoor#ran","syntology_url":"https://syntology.ai/paper/2105.08756","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.08756"}},"official":{"repos":["google-research/pathdreamer"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/optimal-cost-design-for-model-predictive","slug":"optimal-cost-design-for-model-predictive","title":"Optimal Cost Design for Model Predictive Control","date":"2021-04-23","arxiv_id":"2104.11353","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/optimal-cost-design-for-model-predictive#ran","syntology_url":"https://syntology.ai/paper/2104.11353","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.11353"}},"official":{"repos":["avikj/l4dc-mpc-ocd"],"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/contrastive-learning-improves-model","slug":"contrastive-learning-improves-model","title":"Contrastive Learning Improves Model Robustness Under Label Noise","date":"2021-04-19","arxiv_id":"2104.08984","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/contrastive-learning-improves-model#ran","syntology_url":"https://syntology.ai/paper/2104.08984","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.08984"}},"official":{"repos":["arghosh/noisy_label_pretrain"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/achieving-model-robustness-through-discrete","slug":"achieving-model-robustness-through-discrete","title":"Achieving Model Robustness through Discrete Adversarial Training","date":"2021-04-11","arxiv_id":"2104.05062","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/achieving-model-robustness-through-discrete#ran","syntology_url":"https://syntology.ai/paper/2104.05062","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.05062"}},"official":{"repos":["Mivg/robust_transformers"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/model-contrastive-federated-learning","slug":"model-contrastive-federated-learning","title":"Model-Contrastive Federated Learning","date":"2021-03-30","arxiv_id":"2103.16257","repositories_listed":6,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/model-contrastive-federated-learning#ran","syntology_url":"https://syntology.ai/paper/2103.16257","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16257"}},"official":{"repos":["adap/flower"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","named_in_paper"]}}},{"url":"/paper/checkerboard-context-model-for-efficient","slug":"checkerboard-context-model-for-efficient","title":"Checkerboard Context Model for Efficient Learned Image Compression","date":"2021-03-29","arxiv_id":"2103.15306","repositories_listed":4,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"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 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/checkerboard-context-model-for-efficient#ran","syntology_url":"https://syntology.ai/paper/2103.15306","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.15306"}},"official":null}},{"url":"/paper/fast-and-accurate-model-scaling","slug":"fast-and-accurate-model-scaling","title":"Fast and Accurate Model Scaling","date":"2021-03-11","arxiv_id":"2103.06877","repositories_listed":6,"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":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) · 0 unverified","sample_list":"/paper/fast-and-accurate-model-scaling#ran","syntology_url":"https://syntology.ai/paper/2103.06877","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.06877"}},"official":{"repos":["facebookresearch/pycls"],"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/active-testing-sample-efficient-model","slug":"active-testing-sample-efficient-model","title":"Active Testing: Sample-Efficient Model Evaluation","date":"2021-03-09","arxiv_id":"2103.05331","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/active-testing-sample-efficient-model#ran","syntology_url":"https://syntology.ai/paper/2103.05331","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.05331"}},"official":{"repos":["jlko/active-testing"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/swagan-a-style-based-wavelet-driven","slug":"swagan-a-style-based-wavelet-driven","title":"SWAGAN: A Style-based Wavelet-driven Generative Model","date":"2021-02-11","arxiv_id":"2102.06108","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/swagan-a-style-based-wavelet-driven#ran","syntology_url":"https://syntology.ai/paper/2102.06108","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.06108"}},"official":null}},{"url":"/paper/personalized-federated-learning-with-first-1","slug":"personalized-federated-learning-with-first-1","title":"Personalized Federated Learning with First Order Model Optimization","date":"2020-12-15","arxiv_id":"2012.08565","repositories_listed":3,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/personalized-federated-learning-with-first-1#ran","syntology_url":"https://syntology.ai/paper/2012.08565","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.08565"}},"official":{"repos":["NVlabs/FedFomo"],"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/meme-generating-rnn-model-explanations-via","slug":"meme-generating-rnn-model-explanations-via","title":"MEME: Generating RNN Model Explanations via Model Extraction","date":"2020-12-13","arxiv_id":"2012.06954","repositories_listed":1,"syntology":{"n":14,"n_ran":7,"n_constructed":5,"n_ran_checked":5,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":14,"phrase":"7 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/meme-generating-rnn-model-explanations-via#ran","syntology_url":"https://syntology.ai/paper/2012.06954","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.06954"}},"official":{"repos":["dmitrykazhdan/MEME-RNN-XAI"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/fairbatch-batch-selection-for-model-fairness-1","slug":"fairbatch-batch-selection-for-model-fairness-1","title":"FairBatch: Batch Selection for Model Fairness","date":"2020-12-03","arxiv_id":"2012.01696","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/fairbatch-batch-selection-for-model-fairness-1#ran","syntology_url":"https://syntology.ai/paper/2012.01696","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.01696"}},"official":{"repos":["yuji-roh/fairbatch"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/charbert-character-aware-pre-trained-language","slug":"charbert-character-aware-pre-trained-language","title":"CharBERT: Character-aware Pre-trained Language Model","date":"2020-11-03","arxiv_id":"2011.01513","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":6,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/charbert-character-aware-pre-trained-language#ran","syntology_url":"https://syntology.ai/paper/2011.01513","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.01513"}},"official":{"repos":["wtma/CharBERT"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/passport-aware-normalization-for-deep-model","slug":"passport-aware-normalization-for-deep-model","title":"Passport-aware Normalization for Deep Model Protection","date":"2020-10-29","arxiv_id":"2010.15824","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/passport-aware-normalization-for-deep-model#ran","syntology_url":"https://syntology.ai/paper/2010.15824","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.15824"}},"official":{"repos":["ZJZAC/Passport-aware-Normalization"],"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/model-based-policy-optimization-with","slug":"model-based-policy-optimization-with","title":"Model-based Policy Optimization with Unsupervised Model Adaptation","date":"2020-10-19","arxiv_id":"2010.09546","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/model-based-policy-optimization-with#ran","syntology_url":"https://syntology.ai/paper/2010.09546","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.09546"}},"official":{"repos":["RockySJ/ampo"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/model-selection-for-cross-lingual-transfer-1","slug":"model-selection-for-cross-lingual-transfer-1","title":"Model Selection for Cross-Lingual Transfer","date":"2020-10-13","arxiv_id":"2010.06127","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/model-selection-for-cross-lingual-transfer-1#ran","syntology_url":"https://syntology.ai/paper/2010.06127","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.06127"}},"official":{"repos":["edchengg/model_selection"],"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/regularizing-neural-networks-via-adversarial","slug":"regularizing-neural-networks-via-adversarial","title":"Regularizing Neural Networks via Adversarial Model Perturbation","date":"2020-10-10","arxiv_id":"2010.04925","repositories_listed":1,"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/regularizing-neural-networks-via-adversarial#ran","syntology_url":"https://syntology.ai/paper/2010.04925","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.04925"}},"official":{"repos":["hiyouga/AMP-Regularizer"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/improved-techniques-for-model-inversion-1","slug":"improved-techniques-for-model-inversion-1","title":"Knowledge-Enriched Distributional Model Inversion Attacks","date":"2020-10-08","arxiv_id":"2010.04092","repositories_listed":2,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":2,"n_instrument":5,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/improved-techniques-for-model-inversion-1#ran","syntology_url":"https://syntology.ai/paper/2010.04092","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.04092"}},"official":{"repos":["scccc21/knowledge-enriched-dmi","SCccc21/Knowledge-Enriched-Distributional-Model-Inversion-Attacks"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-model-enhanced-human-motion-1","slug":"generative-model-enhanced-human-motion-1","title":"Generative Model-Enhanced Human Motion Prediction","date":"2020-10-05","arxiv_id":"2010.11699","repositories_listed":2,"syntology":{"n":10,"n_ran":6,"n_constructed":5,"n_ran_checked":6,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":3,"phrase":"6 ran (of which 5 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/generative-model-enhanced-human-motion-1#ran","syntology_url":"https://syntology.ai/paper/2010.11699","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.11699"}},"official":{"repos":["bouracha/OoDMotion"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-model-adaptation-for-continual","slug":"unsupervised-model-adaptation-for-continual","title":"Unsupervised Model Adaptation for Continual Semantic Segmentation","date":"2020-09-26","arxiv_id":"2009.12518","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/unsupervised-model-adaptation-for-continual#ran","syntology_url":"https://syntology.ai/paper/2009.12518","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.12518"}},"official":null}},{"url":"/paper/diffwave-a-versatile-diffusion-model-for","slug":"diffwave-a-versatile-diffusion-model-for","title":"DiffWave: A Versatile Diffusion Model for Audio Synthesis","date":"2020-09-21","arxiv_id":"2009.09761","repositories_listed":11,"syntology":{"n":33,"n_ran":20,"n_constructed":11,"n_ran_checked":13,"n_instrument":7,"n_unverified":13,"n_honours":1,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"20 ran (of which 11 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 0 violated, 12 with no contract checked; 7 where Syntology's instrument failed) · 13 unverified","sample_list":"/paper/diffwave-a-versatile-diffusion-model-for#ran","syntology_url":"https://syntology.ai/paper/2009.09761","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.09761"}},"official":null}},{"url":"/paper/neural-sinkhorn-topic-model","slug":"neural-sinkhorn-topic-model","title":"Neural Topic Model via Optimal Transport","date":"2020-08-12","arxiv_id":"2008.13537","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/neural-sinkhorn-topic-model#ran","syntology_url":"https://syntology.ai/paper/2008.13537","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.13537"}},"official":{"repos":["ethanhezhao/NeuralSinkhornTopicModel"],"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/rewriting-a-deep-generative-model","slug":"rewriting-a-deep-generative-model","title":"Rewriting a Deep Generative Model","date":"2020-07-30","arxiv_id":"2007.15646","repositories_listed":3,"syntology":{"n":23,"n_ran":18,"n_constructed":0,"n_ran_checked":13,"n_instrument":5,"n_unverified":5,"n_honours":4,"n_violates":1,"n_no_contract":8,"n_pointer_only":5,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 4 honoured, 1 violated, 8 with no contract checked; 5 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/rewriting-a-deep-generative-model#ran","syntology_url":"https://syntology.ai/paper/2007.15646","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.15646"}},"official":{"repos":["davidbau/rewriting"],"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/model-fusion-with-kullback-leibler-divergence","slug":"model-fusion-with-kullback-leibler-divergence","title":"Model Fusion with Kullback--Leibler Divergence","date":"2020-07-13","arxiv_id":"2007.06168","repositories_listed":1,"syntology":{"n":10,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 5 unverified","sample_list":"/paper/model-fusion-with-kullback-leibler-divergence#ran","syntology_url":"https://syntology.ai/paper/2007.06168","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.06168"}},"official":{"repos":["IBM/KL-fusion"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/segfix-model-agnostic-boundary-refinement-for","slug":"segfix-model-agnostic-boundary-refinement-for","title":"SegFix: Model-Agnostic Boundary Refinement for Segmentation","date":"2020-07-08","arxiv_id":"2007.04269","repositories_listed":4,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/segfix-model-agnostic-boundary-refinement-for#ran","syntology_url":"https://syntology.ai/paper/2007.04269","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.04269"}},"official":{"repos":["openseg-group/openseg.pytorch"],"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/regional-image-perturbation-reduces-l-p-norms","slug":"regional-image-perturbation-reduces-l-p-norms","title":"Regional Image Perturbation Reduces $L_p$ Norms of Adversarial Examples While Maintaining Model-to-model Transferability","date":"2020-07-07","arxiv_id":"2007.03198","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/regional-image-perturbation-reduces-l-p-norms#ran","syntology_url":"https://syntology.ai/paper/2007.03198","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.03198"}},"official":{"repos":["utkuozbulak/regional-adversarial-perturbation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bidirectional-model-based-policy-optimization","slug":"bidirectional-model-based-policy-optimization","title":"Bidirectional Model-based Policy Optimization","date":"2020-07-04","arxiv_id":"2007.01995","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bidirectional-model-based-policy-optimization#ran","syntology_url":"https://syntology.ai/paper/2007.01995","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.01995"}},"official":{"repos":["hanglai/bmpo"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/rexnet-diminishing-representational","slug":"rexnet-diminishing-representational","title":"Rethinking Channel Dimensions for Efficient Model Design","date":"2020-07-02","arxiv_id":"2007.00992","repositories_listed":10,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rexnet-diminishing-representational#ran","syntology_url":"https://syntology.ai/paper/2007.00992","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.00992"}},"official":{"repos":["clovaai/rexnet"],"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/crossmodal-language-grounding-in-an-embodied","slug":"crossmodal-language-grounding-in-an-embodied","title":"Crossmodal Language Grounding in an Embodied Neurocognitive Model","date":"2020-06-24","arxiv_id":"2006.13546","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/crossmodal-language-grounding-in-an-embodied#ran","syntology_url":"https://syntology.ai/paper/2006.13546","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.13546"}},"official":{"repos":["heinrichst/adaptive-mtrnn-grounding"],"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/finbert-a-pretrained-language-model-for","slug":"finbert-a-pretrained-language-model-for","title":"FinBERT: A Pretrained Language Model for Financial Communications","date":"2020-06-15","arxiv_id":"2006.08097","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/finbert-a-pretrained-language-model-for#ran","syntology_url":"https://syntology.ai/paper/2006.08097","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.08097"}},"official":{"repos":["yya518/FinBERT"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-latent-space-energy-based-prior","slug":"learning-latent-space-energy-based-prior","title":"Learning Latent Space Energy-Based Prior Model","date":"2020-06-15","arxiv_id":"2006.08205","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":4,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-latent-space-energy-based-prior#ran","syntology_url":"https://syntology.ai/paper/2006.08205","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.08205"}},"official":null}},{"url":"/paper/speaker-sensitive-response-evaluation-model","slug":"speaker-sensitive-response-evaluation-model","title":"Speaker Sensitive Response Evaluation Model","date":"2020-06-12","arxiv_id":"2006.07015","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/speaker-sensitive-response-evaluation-model#ran","syntology_url":"https://syntology.ai/paper/2006.07015","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.07015"}},"official":{"repos":["NoSyu/SSREM"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/robust-model-training-and-generalisation-with","slug":"robust-model-training-and-generalisation-with","title":"Robust model training and generalisation with Studentising flows","date":"2020-06-11","arxiv_id":"2006.06599","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/robust-model-training-and-generalisation-with#ran","syntology_url":"https://syntology.ai/paper/2006.06599","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.06599"}},"official":null}},{"url":"/paper/mopo-model-based-offline-policy-optimization","slug":"mopo-model-based-offline-policy-optimization","title":"MOPO: Model-based Offline Policy Optimization","date":"2020-05-27","arxiv_id":"2005.13239","repositories_listed":6,"syntology":{"n":8,"n_ran":3,"n_constructed":1,"n_ran_checked":2,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/mopo-model-based-offline-policy-optimization#ran","syntology_url":"https://syntology.ai/paper/2005.13239","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.13239"}},"official":{"repos":["tianheyu927/mopo"],"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/context-aware-dynamics-model-for","slug":"context-aware-dynamics-model-for","title":"Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning","date":"2020-05-14","arxiv_id":"2005.06800","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/context-aware-dynamics-model-for#ran","syntology_url":"https://syntology.ai/paper/2005.06800","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.06800"}},"official":{"repos":["younggyoseo/CaDM"],"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/morel-model-based-offline-reinforcement","slug":"morel-model-based-offline-reinforcement","title":"MOReL : Model-Based Offline Reinforcement Learning","date":"2020-05-12","arxiv_id":"2005.05951","repositories_listed":2,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"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) · 4 unverified","sample_list":"/paper/morel-model-based-offline-reinforcement#ran","syntology_url":"https://syntology.ai/paper/2005.05951","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.05951"}},"official":null}},{"url":"/paper/a-simple-language-model-for-task-oriented","slug":"a-simple-language-model-for-task-oriented","title":"A Simple Language Model for Task-Oriented Dialogue","date":"2020-05-02","arxiv_id":"2005.00796","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":11,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 1 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-simple-language-model-for-task-oriented#ran","syntology_url":"https://syntology.ai/paper/2005.00796","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.00796"}},"official":{"repos":["salesforce/simpletod"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-deep-recurrent-survival-model-for-unbiased","slug":"a-deep-recurrent-survival-model-for-unbiased","title":"A Deep Recurrent Survival Model for Unbiased Ranking","date":"2020-04-30","arxiv_id":"2004.14714","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-deep-recurrent-survival-model-for-unbiased#ran","syntology_url":"https://syntology.ai/paper/2004.14714","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.14714"}},"official":{"repos":["Jinjiarui/DRSR"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/model-based-asynchronous-hyperparameter","slug":"model-based-asynchronous-hyperparameter","title":"Model-based Asynchronous Hyperparameter and Neural Architecture Search","date":"2020-03-24","arxiv_id":"2003.10865","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/model-based-asynchronous-hyperparameter#ran","syntology_url":"https://syntology.ai/paper/2003.10865","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.10865"}},"official":{"repos":["awslabs/syne-tune"],"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/metnet-a-neural-weather-model-for","slug":"metnet-a-neural-weather-model-for","title":"MetNet: A Neural Weather Model for Precipitation Forecasting","date":"2020-03-24","arxiv_id":"2003.12140","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/metnet-a-neural-weather-model-for#ran","syntology_url":"https://syntology.ai/paper/2003.12140","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.12140"}},"official":null}},{"url":"/paper/overinterpretation-reveals-image","slug":"overinterpretation-reveals-image","title":"Overinterpretation reveals image classification model pathologies","date":"2020-03-19","arxiv_id":"2003.08907","repositories_listed":2,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":3,"n_instrument":7,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 7 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/overinterpretation-reveals-image#ran","syntology_url":"https://syntology.ai/paper/2003.08907","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.08907"}},"official":{"repos":["gifford-lab/overinterpretation"],"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":["listed","official"]}}},{"url":"/paper/first-order-motion-model-for-image-animation-1","slug":"first-order-motion-model-for-image-animation-1","title":"First Order Motion Model for Image Animation","date":"2020-02-29","arxiv_id":"2003.00196","repositories_listed":3,"syntology":{"n":10,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/first-order-motion-model-for-image-animation-1#ran","syntology_url":"https://syntology.ai/paper/2003.00196","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.00196"}},"official":{"repos":["AliaksandrSiarohin/first-order-model"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/nonparametric-estimation-in-the-dynamic","slug":"nonparametric-estimation-in-the-dynamic","title":"Nonparametric Estimation in the Dynamic Bradley-Terry Model","date":"2020-02-28","arxiv_id":"2003.00083","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/nonparametric-estimation-in-the-dynamic#ran","syntology_url":"https://syntology.ai/paper/2003.00083","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.00083"}},"official":{"repos":["shamindras/bttv-aistats2020"],"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/model-watermarking-for-image-processing","slug":"model-watermarking-for-image-processing","title":"Model Watermarking for Image Processing Networks","date":"2020-02-25","arxiv_id":"2002.11088","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/model-watermarking-for-image-processing#ran","syntology_url":"https://syntology.ai/paper/2002.11088","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.11088"}},"official":null}},{"url":"/paper/neural-network-compression-framework-for-fast","slug":"neural-network-compression-framework-for-fast","title":"Neural Network Compression Framework for fast model inference","date":"2020-02-20","arxiv_id":"2002.08679","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neural-network-compression-framework-for-fast#ran","syntology_url":"https://syntology.ai/paper/2002.08679","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.08679"}},"official":{"repos":["openvinotoolkit/nncf","openvinotoolkit/nncf_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/robust-quantization-one-model-to-rule-them","slug":"robust-quantization-one-model-to-rule-them","title":"Robust Quantization: One Model to Rule Them All","date":"2020-02-18","arxiv_id":"2002.07686","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/robust-quantization-one-model-to-rule-them#ran","syntology_url":"https://syntology.ai/paper/2002.07686","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.07686"}},"official":{"repos":["moranshkolnik/RobustQuantization"],"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/200302645","slug":"200302645","title":"SentenceMIM: A Latent Variable Language Model","date":"2020-02-18","arxiv_id":"2003.02645","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/200302645#ran","syntology_url":"https://syntology.ai/paper/2003.02645","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.02645"}},"official":null}},{"url":"/paper/realm-retrieval-augmented-language-model-pre","slug":"realm-retrieval-augmented-language-model-pre","title":"REALM: Retrieval-Augmented Language Model Pre-Training","date":"2020-02-10","arxiv_id":"2002.08909","repositories_listed":6,"syntology":{"n":4,"n_ran":4,"n_constructed":1,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":1,"n_pointer_only":0,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 2 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/realm-retrieval-augmented-language-model-pre#ran","syntology_url":"https://syntology.ai/paper/2002.08909","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.08909"}},"official":{"repos":["google-research/language"],"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/robbert-a-dutch-roberta-based-language-model","slug":"robbert-a-dutch-roberta-based-language-model","title":"RobBERT: a Dutch RoBERTa-based Language Model","date":"2020-01-17","arxiv_id":"2001.06286","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/robbert-a-dutch-roberta-based-language-model#ran","syntology_url":"https://syntology.ai/paper/2001.06286","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.06286"}},"official":{"repos":["iPieter/RobBERT"],"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/hydra-preserving-ensemble-diversity-for-model-1","slug":"hydra-preserving-ensemble-diversity-for-model-1","title":"Hydra: Preserving Ensemble Diversity for Model Distillation","date":"2020-01-14","arxiv_id":"2001.04694","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hydra-preserving-ensemble-diversity-for-model-1#ran","syntology_url":"https://syntology.ai/paper/2001.04694","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.04694"}},"official":null}},{"url":"/paper/discrimination-aware-network-pruning-for-deep","slug":"discrimination-aware-network-pruning-for-deep","title":"Discrimination-aware Network Pruning for Deep Model Compression","date":"2020-01-04","arxiv_id":"2001.01050","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/discrimination-aware-network-pruning-for-deep#ran","syntology_url":"https://syntology.ai/paper/2001.01050","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.01050"}},"official":{"repos":["SCUT-AILab/DCP"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/bertje-a-dutch-bert-model","slug":"bertje-a-dutch-bert-model","title":"BERTje: A Dutch BERT Model","date":"2019-12-19","arxiv_id":"1912.09582","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/bertje-a-dutch-bert-model#ran","syntology_url":"https://syntology.ai/paper/1912.09582","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.09582"}},"official":{"repos":["wietsedv/bertje"],"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":["listed","official"]}}},{"url":"/paper/flaubert-unsupervised-language-model-pre","slug":"flaubert-unsupervised-language-model-pre","title":"FlauBERT: Unsupervised Language Model Pre-training for French","date":"2019-12-11","arxiv_id":"1912.05372","repositories_listed":7,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/flaubert-unsupervised-language-model-pre#ran","syntology_url":"https://syntology.ai/paper/1912.05372","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.05372"}},"official":{"repos":["getalp/Flaubert"],"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/image-based-table-recognition-data-model-and","slug":"image-based-table-recognition-data-model-and","title":"Image-based table recognition: data, model, and evaluation","date":"2019-11-25","arxiv_id":"1911.10683","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":1,"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/image-based-table-recognition-data-model-and#ran","syntology_url":"https://syntology.ai/paper/1911.10683","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.10683"}},"official":{"repos":["ibm-aur-nlp/PubTabNet"],"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/statistical-model-aggregation-via-parameter","slug":"statistical-model-aggregation-via-parameter","title":"Statistical Model Aggregation via Parameter Matching","date":"2019-11-01","arxiv_id":"1911.00218","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/statistical-model-aggregation-via-parameter#ran","syntology_url":"https://syntology.ai/paper/1911.00218","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.00218"}},"official":{"repos":["IBM/SPAHM"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pseudolikelihood-reranking-with-masked","slug":"pseudolikelihood-reranking-with-masked","title":"Masked Language Model Scoring","date":"2019-10-31","arxiv_id":"1910.14659","repositories_listed":6,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"phrase":"10 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pseudolikelihood-reranking-with-masked#ran","syntology_url":"https://syntology.ai/paper/1910.14659","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.14659"}},"official":{"repos":["awslabs/mlm-scoring"],"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/entity-abstraction-in-visual-model-based","slug":"entity-abstraction-in-visual-model-based","title":"Entity Abstraction in Visual Model-Based Reinforcement Learning","date":"2019-10-28","arxiv_id":"1910.12827","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"5 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/entity-abstraction-in-visual-model-based#ran","syntology_url":"https://syntology.ai/paper/1910.12827","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.12827"}},"official":{"repos":["jcoreyes/OP3"],"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/kernel-stein-tests-for-multiple-model","slug":"kernel-stein-tests-for-multiple-model","title":"Kernel Stein Tests for Multiple Model Comparison","date":"2019-10-27","arxiv_id":"1910.12252","repositories_listed":3,"syntology":{"n":18,"n_ran":18,"n_constructed":0,"n_ran_checked":18,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":18,"n_pointer_only":0,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 0 violated, 18 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/kernel-stein-tests-for-multiple-model#ran","syntology_url":"https://syntology.ai/paper/1910.12252","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.12252"}},"official":{"repos":["jenninglim/model-comparison-test"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/cxplain-causal-explanations-for-model","slug":"cxplain-causal-explanations-for-model","title":"CXPlain: Causal Explanations for Model Interpretation under Uncertainty","date":"2019-10-27","arxiv_id":"1910.12336","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cxplain-causal-explanations-for-model#ran","syntology_url":"https://syntology.ai/paper/1910.12336","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.12336"}},"official":{"repos":["d909b/cxplain"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/intracranial-hemorrhage-segmentation-using","slug":"intracranial-hemorrhage-segmentation-using","title":"Intracranial Hemorrhage Segmentation Using Deep Convolutional Model","date":"2019-10-18","arxiv_id":"1910.08643","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/intracranial-hemorrhage-segmentation-using#ran","syntology_url":"https://syntology.ai/paper/1910.08643","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.08643"}},"official":{"repos":["Murtadha44/-Intracranial-Hemorrhage-Segmentation-Using-Deep-Convolutional-Model-U-Net-"],"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/model-fusion-via-optimal-transport","slug":"model-fusion-via-optimal-transport","title":"Model Fusion via Optimal Transport","date":"2019-10-12","arxiv_id":"1910.05653","repositories_listed":2,"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/model-fusion-via-optimal-transport#ran","syntology_url":"https://syntology.ai/paper/1910.05653","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.05653"}},"official":{"repos":["sidak/otfusion"],"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/fedmd-heterogenous-federated-learning-via","slug":"fedmd-heterogenous-federated-learning-via","title":"FedMD: Heterogenous Federated Learning via Model Distillation","date":"2019-10-08","arxiv_id":"1910.03581","repositories_listed":7,"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/fedmd-heterogenous-federated-learning-via#ran","syntology_url":"https://syntology.ai/paper/1910.03581","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.03581"}},"official":null}},{"url":"/paper/synthesizing-action-sequences-for-modifying","slug":"synthesizing-action-sequences-for-modifying","title":"Synthesizing Action Sequences for Modifying Model Decisions","date":"2019-09-30","arxiv_id":"1910.00057","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/synthesizing-action-sequences-for-modifying#ran","syntology_url":"https://syntology.ai/paper/1910.00057","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.00057"}},"official":null}},{"url":"/paper/deep-model-transferability-from-attribution","slug":"deep-model-transferability-from-attribution","title":"Deep Model Transferability from Attribution Maps","date":"2019-09-26","arxiv_id":"1909.11902","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-model-transferability-from-attribution#ran","syntology_url":"https://syntology.ai/paper/1909.11902","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.11902"}},"official":{"repos":["zju-vipa/TransferbilityFromAttributionMaps"],"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/a-generative-model-for-molecular-distance-1","slug":"a-generative-model-for-molecular-distance-1","title":"A Generative Model for Molecular Distance Geometry","date":"2019-09-25","arxiv_id":"1909.11459","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-generative-model-for-molecular-distance-1#ran","syntology_url":"https://syntology.ai/paper/1909.11459","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.11459"}},"official":{"repos":["gncs/graphdg"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/patient-knowledge-distillation-for-bert-model","slug":"patient-knowledge-distillation-for-bert-model","title":"Patient Knowledge Distillation for BERT Model Compression","date":"2019-08-25","arxiv_id":"1908.09355","repositories_listed":5,"syntology":{"n":27,"n_ran":19,"n_constructed":0,"n_ran_checked":13,"n_instrument":6,"n_unverified":8,"n_honours":2,"n_violates":1,"n_no_contract":10,"n_pointer_only":27,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 2 honoured, 1 violated, 10 with no contract checked; 6 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/patient-knowledge-distillation-for-bert-model#ran","syntology_url":"https://syntology.ai/paper/1908.09355","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.09355"}},"official":{"repos":["intersun/PKD-for-BERT-Model-Compression"],"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/a-self-attentive-model-for-knowledge-tracing","slug":"a-self-attentive-model-for-knowledge-tracing","title":"A Self-Attentive model for Knowledge Tracing","date":"2019-07-16","arxiv_id":"1907.06837","repositories_listed":10,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":2,"n_no_contract":2,"n_pointer_only":9,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 2 violated, 2 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/a-self-attentive-model-for-knowledge-tracing#ran","syntology_url":"https://syntology.ai/paper/1907.06837","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.06837"}},"official":null}},{"url":"/paper/the-dynamic-embedded-topic-model","slug":"the-dynamic-embedded-topic-model","title":"The Dynamic Embedded Topic Model","date":"2019-07-12","arxiv_id":"1907.05545","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/the-dynamic-embedded-topic-model#ran","syntology_url":"https://syntology.ai/paper/1907.05545","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.05545"}},"official":{"repos":["adjidieng/DETM"],"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/when-to-trust-your-model-model-based-policy","slug":"when-to-trust-your-model-model-based-policy","title":"When to Trust Your Model: Model-Based Policy Optimization","date":"2019-06-19","arxiv_id":"1906.08253","repositories_listed":11,"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/when-to-trust-your-model-model-based-policy#ran","syntology_url":"https://syntology.ai/paper/1906.08253","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.08253"}},"official":{"repos":["JannerM/mbpo"],"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/190600531","slug":"190600531","title":"Model selection for contextual bandits","date":"2019-06-03","arxiv_id":"1906.00531","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/190600531#ran","syntology_url":"https://syntology.ai/paper/1906.00531","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.00531"}},"official":{"repos":["akshaykr/oracle_cb"],"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/efficientnet-rethinking-model-scaling-for","slug":"efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","arxiv_id":"1905.11946","repositories_listed":144,"syntology":{"n":302,"n_ran":198,"n_constructed":73,"n_ran_checked":157,"n_instrument":41,"n_unverified":104,"n_honours":26,"n_violates":2,"n_no_contract":129,"n_pointer_only":113,"phrase":"198 ran (of which 73 constructed an object rather than computing a result; 157 with no instrument failure: 26 honoured, 2 violated, 129 with no contract checked; 41 where Syntology's instrument failed) · 104 unverified","sample_list":"/paper/efficientnet-rethinking-model-scaling-for#ran","syntology_url":"https://syntology.ai/paper/1905.11946","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.11946"}},"official":{"repos":["tensorflow/tpu"],"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/differential-privacy-has-disparate-impact-on","slug":"differential-privacy-has-disparate-impact-on","title":"Differential Privacy Has Disparate Impact on Model Accuracy","date":"2019-05-28","arxiv_id":"1905.12101","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/differential-privacy-has-disparate-impact-on#ran","syntology_url":"https://syntology.ai/paper/1905.12101","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.12101"}},"official":{"repos":["ebagdasa/differential-privacy-vs-fairness"],"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/190510350","slug":"190510350","title":"Unsupervised Community Detection with Modularity-Based Attention Model","date":"2019-05-20","arxiv_id":"1905.10350","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/190510350#ran","syntology_url":"https://syntology.ai/paper/1905.10350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.10350"}},"official":{"repos":["Ivanopolo/modnet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/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/what-is-wrong-with-scene-text-recognition","slug":"what-is-wrong-with-scene-text-recognition","title":"What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis","date":"2019-04-03","arxiv_id":"1904.01906","repositories_listed":13,"syntology":{"n":19,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":0,"phrase":"17 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; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/what-is-wrong-with-scene-text-recognition#ran","syntology_url":"https://syntology.ai/paper/1904.01906","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.01906"}},"official":{"repos":["clovaai/deep-text-recognition-benchmark"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/75-languages-1-model-parsing-universal","slug":"75-languages-1-model-parsing-universal","title":"75 Languages, 1 Model: Parsing Universal Dependencies Universally","date":"2019-04-03","arxiv_id":"1904.02099","repositories_listed":3,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/75-languages-1-model-parsing-universal#ran","syntology_url":"https://syntology.ai/paper/1904.02099","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.02099"}},"official":{"repos":["hyperparticle/udify"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/class-incremental-learning-via-deep-model","slug":"class-incremental-learning-via-deep-model","title":"Class-incremental Learning via Deep Model Consolidation","date":"2019-03-19","arxiv_id":"1903.07864","repositories_listed":2,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":8,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/class-incremental-learning-via-deep-model#ran","syntology_url":"https://syntology.ai/paper/1903.07864","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.07864"}},"official":null}},{"url":"/paper/model-primitive-hierarchical-lifelong","slug":"model-primitive-hierarchical-lifelong","title":"Model Primitive Hierarchical Lifelong Reinforcement Learning","date":"2019-03-04","arxiv_id":"1903.01567","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":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) · 0 unverified","sample_list":"/paper/model-primitive-hierarchical-lifelong#ran","syntology_url":"https://syntology.ai/paper/1903.01567","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.01567"}},"official":{"repos":["sisl/MPHRL"],"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/model-based-reinforcement-learning-for-atari","slug":"model-based-reinforcement-learning-for-atari","title":"Model-Based Reinforcement Learning for Atari","date":"2019-03-01","arxiv_id":"1903.00374","repositories_listed":2,"syntology":{"n":21,"n_ran":16,"n_constructed":8,"n_ran_checked":10,"n_instrument":6,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":17,"phrase":"16 ran (of which 8 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 6 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/model-based-reinforcement-learning-for-atari#ran","syntology_url":"https://syntology.ai/paper/1903.00374","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.00374"}},"official":{"repos":["tensorflow/tensor2tensor"],"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":["listed","official"]}}},{"url":"/paper/evaluating-model-calibration-in","slug":"evaluating-model-calibration-in","title":"Evaluating model calibration in classification","date":"2019-02-19","arxiv_id":"1902.06977","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/evaluating-model-calibration-in#ran","syntology_url":"https://syntology.ai/paper/1902.06977","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.06977"}},"official":{"repos":["uu-sml/calibration"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/wasserstein-barycenter-model-ensembling-1","slug":"wasserstein-barycenter-model-ensembling-1","title":"Wasserstein Barycenter Model Ensembling","date":"2019-02-13","arxiv_id":"1902.04999","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/wasserstein-barycenter-model-ensembling-1#ran","syntology_url":"https://syntology.ai/paper/1902.04999","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.04999"}},"official":null}},{"url":"/paper/cross-lingual-language-model-pretraining","slug":"cross-lingual-language-model-pretraining","title":"Cross-lingual Language Model Pretraining","date":"2019-01-22","arxiv_id":"1901.07291","repositories_listed":17,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/cross-lingual-language-model-pretraining#ran","syntology_url":"https://syntology.ai/paper/1901.07291","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.07291"}},"official":null}},{"url":"/paper/divergence-triangle-for-joint-training-of","slug":"divergence-triangle-for-joint-training-of","title":"Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model","date":"2018-12-28","arxiv_id":"1812.10907","repositories_listed":1,"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":4,"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/divergence-triangle-for-joint-training-of#ran","syntology_url":"https://syntology.ai/paper/1812.10907","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.10907"}},"official":null}},{"url":"/paper/an-empirical-model-of-large-batch-training","slug":"an-empirical-model-of-large-batch-training","title":"An Empirical Model of Large-Batch Training","date":"2018-12-14","arxiv_id":"1812.06162","repositories_listed":11,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/an-empirical-model-of-large-batch-training#ran","syntology_url":"https://syntology.ai/paper/1812.06162","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.06162"}},"official":null}},{"url":"/paper/online-model-distillation-for-efficient-video","slug":"online-model-distillation-for-efficient-video","title":"Online Model Distillation for Efficient Video Inference","date":"2018-12-06","arxiv_id":"1812.02699","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":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/online-model-distillation-for-efficient-video#ran","syntology_url":"https://syntology.ai/paper/1812.02699","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.02699"}},"official":null}},{"url":"/paper/toward-scalable-neural-dialogue-state","slug":"toward-scalable-neural-dialogue-state","title":"Toward Scalable Neural Dialogue State Tracking Model","date":"2018-12-03","arxiv_id":"1812.00899","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":6,"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/toward-scalable-neural-dialogue-state#ran","syntology_url":"https://syntology.ai/paper/1812.00899","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.00899"}},"official":{"repos":["elnaaz/GCE-Model"],"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/explicit-interaction-model-towards-text","slug":"explicit-interaction-model-towards-text","title":"Explicit Interaction Model towards Text Classification","date":"2018-11-23","arxiv_id":"1811.09386","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/explicit-interaction-model-towards-text#ran","syntology_url":"https://syntology.ai/paper/1811.09386","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.09386"}},"official":{"repos":["NonvolatileMemory/AAAI_2019_EXAM"],"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/model-based-active-exploration","slug":"model-based-active-exploration","title":"Model-Based Active Exploration","date":"2018-10-29","arxiv_id":"1810.12162","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/model-based-active-exploration#ran","syntology_url":"https://syntology.ai/paper/1810.12162","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.12162"}},"official":{"repos":["nnaisense/max"],"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/model-cards-for-model-reporting","slug":"model-cards-for-model-reporting","title":"Model Cards for Model Reporting","date":"2018-10-05","arxiv_id":"1810.03993","repositories_listed":12,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"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) · 4 unverified","sample_list":"/paper/model-cards-for-model-reporting#ran","syntology_url":"https://syntology.ai/paper/1810.03993","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.03993"}},"official":null}},{"url":"/paper/a-neural-temporal-model-for-human-motion","slug":"a-neural-temporal-model-for-human-motion","title":"A Neural Temporal Model for Human Motion Prediction","date":"2018-09-09","arxiv_id":"1809.03036","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":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) · 0 unverified","sample_list":"/paper/a-neural-temporal-model-for-human-motion#ran","syntology_url":"https://syntology.ai/paper/1809.03036","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.03036"}},"official":{"repos":["cr7anand/neural_temporal_models"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/simple-fusion-return-of-the-language-model","slug":"simple-fusion-return-of-the-language-model","title":"Simple Fusion: Return of the Language Model","date":"2018-09-01","arxiv_id":"1809.00125","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":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) · 8 unverified","sample_list":"/paper/simple-fusion-return-of-the-language-model#ran","syntology_url":"https://syntology.ai/paper/1809.00125","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.00125"}},"official":null}},{"url":"/paper/a-neural-attention-model-for-speech-command","slug":"a-neural-attention-model-for-speech-command","title":"A neural attention model for speech command recognition","date":"2018-08-27","arxiv_id":"1808.08929","repositories_listed":8,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-neural-attention-model-for-speech-command#ran","syntology_url":"https://syntology.ai/paper/1808.08929","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.08929"}},"official":{"repos":["douglas125/SpeechCmdRecognition"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/oboe-collaborative-filtering-for-automl","slug":"oboe-collaborative-filtering-for-automl","title":"OBOE: Collaborative Filtering for AutoML Model Selection","date":"2018-08-09","arxiv_id":"1808.03233","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/oboe-collaborative-filtering-for-automl#ran","syntology_url":"https://syntology.ai/paper/1808.03233","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.03233"}},"official":{"repos":["udellgroup/oboe"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-training-for-3d-morphable-model","slug":"unsupervised-training-for-3d-morphable-model","title":"Unsupervised Training for 3D Morphable Model Regression","date":"2018-06-15","arxiv_id":"1806.06098","repositories_listed":2,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":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) · 5 unverified","sample_list":"/paper/unsupervised-training-for-3d-morphable-model#ran","syntology_url":"https://syntology.ai/paper/1806.06098","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.06098"}},"official":{"repos":["google/tf_mesh_renderer"],"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/sgm-sequence-generation-model-for-multi-label","slug":"sgm-sequence-generation-model-for-multi-label","title":"SGM: Sequence Generation Model for Multi-label Classification","date":"2018-06-13","arxiv_id":"1806.04822","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/sgm-sequence-generation-model-for-multi-label#ran","syntology_url":"https://syntology.ai/paper/1806.04822","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.04822"}},"official":{"repos":["lancopku/SGM"],"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"]}}}],"record_sha256":"62210f4d5fb758616ebdf7f83eca4d2eb3fba070bb04d9f69a8e186a74b538bb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}