{"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/image-classification/papers/ran/13","list_of":"/task/image-classification","task":"Image Classification","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":13,"pages_in_order":14,"rows_per_page":100,"rows":[1201,1300],"of":1392,"counts":{"archive_papers_tagged":10488,"with_a_code_link":4702,"where_syntology_ran_a_sample":1392,"not_listed_spam_title":0,"listed":10488,"listed_where_code_ran":1392,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1164,"every_run_a_failure_of_syntologys_instrument":228,"listed_with_a_run_with_no_instrument_failure":1164,"listed_every_run_a_failure_of_syntologys_instrument":228,"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/image-classification/papers/ran/1","prev":"/task/image-classification/papers/ran/12","next":"/task/image-classification/papers/ran/14","papers":[{"url":"/paper/detnas-neural-architecture-search-on-object","slug":"detnas-neural-architecture-search-on-object","title":"DetNAS: Backbone Search for Object Detection","date":"2019-03-26","arxiv_id":"1903.10979","repositories_listed":2,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/detnas-neural-architecture-search-on-object#ran","syntology_url":"https://syntology.ai/paper/1903.10979","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.10979"}},"official":{"repos":["megvii-model/DetNAS"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/sharpdarts-faster-and-more-accurate","slug":"sharpdarts-faster-and-more-accurate","title":"sharpDARTS: Faster and More Accurate Differentiable Architecture Search","date":"2019-03-23","arxiv_id":"1903.09900","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/sharpdarts-faster-and-more-accurate#ran","syntology_url":"https://syntology.ai/paper/1903.09900","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.09900"}},"official":{"repos":["ahundt/sharpDARTS"],"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/probabilistic-end-to-end-noise-correction-for","slug":"probabilistic-end-to-end-noise-correction-for","title":"Probabilistic End-to-end Noise Correction for Learning with Noisy Labels","date":"2019-03-19","arxiv_id":"1903.07788","repositories_listed":3,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/probabilistic-end-to-end-noise-correction-for#ran","syntology_url":"https://syntology.ai/paper/1903.07788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.07788"}},"official":{"repos":["yikun2019/PENCIL"],"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/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/selective-kernel-networks","slug":"selective-kernel-networks","title":"Selective Kernel Networks","date":"2019-03-15","arxiv_id":"1903.06586","repositories_listed":20,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/selective-kernel-networks#ran","syntology_url":"https://syntology.ai/paper/1903.06586","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.06586"}},"official":{"repos":["implus/SKNet"],"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/all-you-need-is-a-few-shifts-designing","slug":"all-you-need-is-a-few-shifts-designing","title":"All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification","date":"2019-03-13","arxiv_id":"1903.05285","repositories_listed":3,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/all-you-need-is-a-few-shifts-designing#ran","syntology_url":"https://syntology.ai/paper/1903.05285","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.05285"}},"official":{"repos":["hikvision-research/SparseShiftLayer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/deepobs-a-deep-learning-optimizer-benchmark-1","slug":"deepobs-a-deep-learning-optimizer-benchmark-1","title":"DeepOBS: A Deep Learning Optimizer Benchmark Suite","date":"2019-03-13","arxiv_id":"1903.05499","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/deepobs-a-deep-learning-optimizer-benchmark-1#ran","syntology_url":"https://syntology.ai/paper/1903.05499","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.05499"}},"official":{"repos":["fsschneider/deepobs"],"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/structured-knowledge-distillation-for","slug":"structured-knowledge-distillation-for","title":"Structured Knowledge Distillation for Dense Prediction","date":"2019-03-11","arxiv_id":"1903.04197","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":1,"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; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/structured-knowledge-distillation-for#ran","syntology_url":"https://syntology.ai/paper/1903.04197","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.04197"}},"official":{"repos":["irfanICMLL/structure_knowledge_distillation"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/weakly-supervised-complementary-parts-models","slug":"weakly-supervised-complementary-parts-models","title":"Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up","date":"2019-03-07","arxiv_id":"1903.02827","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/weakly-supervised-complementary-parts-models#ran","syntology_url":"https://syntology.ai/paper/1903.02827","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.02827"}},"official":null}},{"url":"/paper/statistical-guarantees-for-the-robustness-of","slug":"statistical-guarantees-for-the-robustness-of","title":"Statistical Guarantees for the Robustness of Bayesian Neural Networks","date":"2019-03-05","arxiv_id":"1903.01980","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/statistical-guarantees-for-the-robustness-of#ran","syntology_url":"https://syntology.ai/paper/1903.01980","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.01980"}},"official":{"repos":["matthewwicker/StatisticalGuarenteesForBNNs"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/wasserstein-adversarial-examples-via","slug":"wasserstein-adversarial-examples-via","title":"Wasserstein Adversarial Examples via Projected Sinkhorn Iterations","date":"2019-02-21","arxiv_id":"1902.07906","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":2,"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: 1 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/wasserstein-adversarial-examples-via#ran","syntology_url":"https://syntology.ai/paper/1902.07906","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.07906"}},"official":{"repos":["locuslab/projected_sinkhorn"],"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/push-the-student-to-learn-right-progressive","slug":"push-the-student-to-learn-right-progressive","title":"Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting","date":"2019-02-20","arxiv_id":"1902.07379","repositories_listed":3,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/push-the-student-to-learn-right-progressive#ran","syntology_url":"https://syntology.ai/paper/1902.07379","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.07379"}},"official":{"repos":["xjtushujun/meta-weight-net"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/adaptive-cross-modal-few-shot-learning","slug":"adaptive-cross-modal-few-shot-learning","title":"Adaptive Cross-Modal Few-Shot Learning","date":"2019-02-19","arxiv_id":"1902.07104","repositories_listed":1,"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/adaptive-cross-modal-few-shot-learning#ran","syntology_url":"https://syntology.ai/paper/1902.07104","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.07104"}},"official":null}},{"url":"/paper/simplifying-graph-convolutional-networks","slug":"simplifying-graph-convolutional-networks","title":"Simplifying Graph Convolutional Networks","date":"2019-02-19","arxiv_id":"1902.07153","repositories_listed":7,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/simplifying-graph-convolutional-networks#ran","syntology_url":"https://syntology.ai/paper/1902.07153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.07153"}},"official":{"repos":["Tiiiger/SGC"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/contextual-encoder-decoder-network-for-visual","slug":"contextual-encoder-decoder-network-for-visual","title":"Contextual Encoder-Decoder Network for Visual Saliency Prediction","date":"2019-02-18","arxiv_id":"1902.06634","repositories_listed":4,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/contextual-encoder-decoder-network-for-visual#ran","syntology_url":"https://syntology.ai/paper/1902.06634","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.06634"}},"official":{"repos":["alexanderkroner/saliency"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed"]}}},{"url":"/paper/multigrain-a-unified-image-embedding-for","slug":"multigrain-a-unified-image-embedding-for","title":"MultiGrain: a unified image embedding for classes and instances","date":"2019-02-14","arxiv_id":"1902.05509","repositories_listed":3,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multigrain-a-unified-image-embedding-for#ran","syntology_url":"https://syntology.ai/paper/1902.05509","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.05509"}},"official":{"repos":["facebookresearch/multigrain"],"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/bag-of-freebies-for-training-object-detection","slug":"bag-of-freebies-for-training-object-detection","title":"Bag of Freebies for Training Object Detection Neural Networks","date":"2019-02-11","arxiv_id":"1902.04103","repositories_listed":3,"syntology":{"n":18,"n_ran":15,"n_constructed":0,"n_ran_checked":14,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/bag-of-freebies-for-training-object-detection#ran","syntology_url":"https://syntology.ai/paper/1902.04103","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.04103"}},"official":{"repos":["dmlc/gluon-cv"],"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/meta-amortized-variational-inference-and","slug":"meta-amortized-variational-inference-and","title":"Meta-Amortized Variational Inference and Learning","date":"2019-02-05","arxiv_id":"1902.01950","repositories_listed":1,"syntology":{"n":15,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":1,"n_no_contract":8,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/meta-amortized-variational-inference-and#ran","syntology_url":"https://syntology.ai/paper/1902.01950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.01950"}},"official":null}},{"url":"/paper/parameter-efficient-transfer-learning-for-nlp","slug":"parameter-efficient-transfer-learning-for-nlp","title":"Parameter-Efficient Transfer Learning for NLP","date":"2019-02-02","arxiv_id":"1902.00751","repositories_listed":17,"syntology":{"n":22,"n_ran":14,"n_constructed":3,"n_ran_checked":14,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":3,"phrase":"14 ran (of which 3 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/parameter-efficient-transfer-learning-for-nlp#ran","syntology_url":"https://syntology.ai/paper/1902.00751","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.00751"}},"official":{"repos":["google-research/adapter-bert"],"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/glyce-glyph-vectors-for-chinese-character","slug":"glyce-glyph-vectors-for-chinese-character","title":"Glyce: Glyph-vectors for Chinese Character Representations","date":"2019-01-29","arxiv_id":"1901.10125","repositories_listed":2,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/glyce-glyph-vectors-for-chinese-character#ran","syntology_url":"https://syntology.ai/paper/1901.10125","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.10125"}},"official":{"repos":["ShannonAI/glyce"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fixup-initialization-residual-learning","slug":"fixup-initialization-residual-learning","title":"Fixup Initialization: Residual Learning Without Normalization","date":"2019-01-27","arxiv_id":"1901.09321","repositories_listed":10,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/fixup-initialization-residual-learning#ran","syntology_url":"https://syntology.ai/paper/1901.09321","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.09321"}},"official":null}},{"url":"/paper/equivariant-transformer-networks","slug":"equivariant-transformer-networks","title":"Equivariant Transformer Networks","date":"2019-01-25","arxiv_id":"1901.11399","repositories_listed":3,"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/equivariant-transformer-networks#ran","syntology_url":"https://syntology.ai/paper/1901.11399","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.11399"}},"official":{"repos":["stanford-futuredata/equivariant-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/decoupled-greedy-learning-of-cnns","slug":"decoupled-greedy-learning-of-cnns","title":"Decoupled Greedy Learning of CNNs","date":"2019-01-23","arxiv_id":"1901.08164","repositories_listed":2,"syntology":{"n":14,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/decoupled-greedy-learning-of-cnns#ran","syntology_url":"https://syntology.ai/paper/1901.08164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.08164"}},"official":{"repos":["eugenium/DGL"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/calibration-with-bias-corrected-temperature","slug":"calibration-with-bias-corrected-temperature","title":"Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation","date":"2019-01-21","arxiv_id":"1901.06852","repositories_listed":3,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":3,"n_violates":2,"n_no_contract":0,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 3 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/calibration-with-bias-corrected-temperature#ran","syntology_url":"https://syntology.ai/paper/1901.06852","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.06852"}},"official":{"repos":["kundajelab/abstention","blindauth/labelshiftexperiments","kundajelab/labelshiftexperiments"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/class-balanced-loss-based-on-effective-number","slug":"class-balanced-loss-based-on-effective-number","title":"Class-Balanced Loss Based on Effective Number of Samples","date":"2019-01-16","arxiv_id":"1901.05555","repositories_listed":11,"syntology":{"n":27,"n_ran":15,"n_constructed":2,"n_ran_checked":13,"n_instrument":2,"n_unverified":12,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"15 ran (of which 2 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 2 where Syntology's instrument failed) · 12 unverified","sample_list":"/paper/class-balanced-loss-based-on-effective-number#ran","syntology_url":"https://syntology.ai/paper/1901.05555","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.05555"}},"official":{"repos":["richardaecn/class-balanced-loss"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":11,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/auto-deeplab-hierarchical-neural-architecture","slug":"auto-deeplab-hierarchical-neural-architecture","title":"Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation","date":"2019-01-10","arxiv_id":"1901.02985","repositories_listed":12,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/auto-deeplab-hierarchical-neural-architecture#ran","syntology_url":"https://syntology.ai/paper/1901.02985","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.02985"}},"official":{"repos":["tensorflow/models"],"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-comprehensive-guide-to-bayesian","slug":"a-comprehensive-guide-to-bayesian","title":"A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference","date":"2019-01-08","arxiv_id":"1901.02731","repositories_listed":6,"syntology":{"n":14,"n_ran":14,"n_constructed":0,"n_ran_checked":10,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":2,"phrase":"14 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-comprehensive-guide-to-bayesian#ran","syntology_url":"https://syntology.ai/paper/1901.02731","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.02731"}},"official":{"repos":["kumar-shridhar/PyTorch-BayesianCNN"],"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":["listed","official"]}}},{"url":"/paper/learning-efficient-detector-with-semi","slug":"learning-efficient-detector-with-semi","title":"Learning Efficient Detector with Semi-supervised Adaptive Distillation","date":"2019-01-02","arxiv_id":"1901.00366","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-efficient-detector-with-semi#ran","syntology_url":"https://syntology.ai/paper/1901.00366","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.00366"}},"official":{"repos":["Tangshitao/Semi-supervised-Adaptive-Distillation"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/attention-branch-network-learning-of","slug":"attention-branch-network-learning-of","title":"Attention Branch Network: Learning of Attention Mechanism for Visual Explanation","date":"2018-12-25","arxiv_id":"1812.10025","repositories_listed":3,"syntology":{"n":24,"n_ran":16,"n_constructed":0,"n_ran_checked":14,"n_instrument":2,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":2,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 2 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/attention-branch-network-learning-of#ran","syntology_url":"https://syntology.ai/paper/1812.10025","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.10025"}},"official":{"repos":["machine-perception-robotics-group/attention_branch_network"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/guessing-smart-biased-sampling-for-efficient","slug":"guessing-smart-biased-sampling-for-efficient","title":"Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks","date":"2018-12-24","arxiv_id":"1812.09803","repositories_listed":3,"syntology":{"n":10,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/guessing-smart-biased-sampling-for-efficient#ran","syntology_url":"https://syntology.ai/paper/1812.09803","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.09803"}},"official":{"repos":["ttbrunner/biased_boundary_attack","ttbrunner/biased_boundary_attack_avc"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/fbnet-hardware-aware-efficient-convnet-design","slug":"fbnet-hardware-aware-efficient-convnet-design","title":"FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search","date":"2018-12-09","arxiv_id":"1812.03443","repositories_listed":5,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fbnet-hardware-aware-efficient-convnet-design#ran","syntology_url":"https://syntology.ai/paper/1812.03443","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.03443"}},"official":{"repos":["facebookresearch/mobile-vision"],"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/few-shot-object-detection-via-feature","slug":"few-shot-object-detection-via-feature","title":"Few-shot Object Detection via Feature Reweighting","date":"2018-12-05","arxiv_id":"1812.01866","repositories_listed":4,"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/few-shot-object-detection-via-feature#ran","syntology_url":"https://syntology.ai/paper/1812.01866","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.01866"}},"official":{"repos":["bingykang/Fewshot_Detection"],"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/bag-of-tricks-for-image-classification-with","slug":"bag-of-tricks-for-image-classification-with","title":"Bag of Tricks for Image Classification with Convolutional Neural Networks","date":"2018-12-04","arxiv_id":"1812.01187","repositories_listed":28,"syntology":{"n":15,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":4,"n_honours":2,"n_violates":0,"n_no_contract":7,"n_pointer_only":5,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/bag-of-tricks-for-image-classification-with#ran","syntology_url":"https://syntology.ai/paper/1812.01187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.01187"}},"official":{"repos":["dmlc/gluon-cv"],"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/factorized-attention-self-attention-with","slug":"factorized-attention-self-attention-with","title":"Efficient Attention: Attention with Linear Complexities","date":"2018-12-04","arxiv_id":"1812.01243","repositories_listed":14,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":4,"n_no_contract":1,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 4 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/factorized-attention-self-attention-with#ran","syntology_url":"https://syntology.ai/paper/1812.01243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.01243"}},"official":{"repos":["cmsflash/efficient-attention"],"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/deep-learning-for-classical-japanese","slug":"deep-learning-for-classical-japanese","title":"Deep Learning for Classical Japanese Literature","date":"2018-12-03","arxiv_id":"1812.01718","repositories_listed":10,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":1,"n_no_contract":6,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 1 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/deep-learning-for-classical-japanese#ran","syntology_url":"https://syntology.ai/paper/1812.01718","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.01718"}},"official":{"repos":["rois-codh/kmnist"],"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/proxylessnas-direct-neural-architecture","slug":"proxylessnas-direct-neural-architecture","title":"ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware","date":"2018-12-02","arxiv_id":"1812.00332","repositories_listed":23,"syntology":{"n":27,"n_ran":17,"n_constructed":0,"n_ran_checked":14,"n_instrument":3,"n_unverified":10,"n_honours":2,"n_violates":0,"n_no_contract":12,"n_pointer_only":4,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 2 honoured, 0 violated, 12 with no contract checked; 3 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/proxylessnas-direct-neural-architecture#ran","syntology_url":"https://syntology.ai/paper/1812.00332","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.00332"}},"official":{"repos":["MIT-HAN-LAB/ProxylessNAS"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/graph-based-global-reasoning-networks","slug":"graph-based-global-reasoning-networks","title":"Graph-Based Global Reasoning Networks","date":"2018-11-30","arxiv_id":"1811.12814","repositories_listed":9,"syntology":{"n":15,"n_ran":15,"n_constructed":0,"n_ran_checked":10,"n_instrument":5,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":7,"phrase":"15 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; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/graph-based-global-reasoning-networks#ran","syntology_url":"https://syntology.ai/paper/1811.12814","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.12814"}},"official":{"repos":["facebookresearch/GloRe"],"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/imagenet-trained-cnns-are-biased-towards","slug":"imagenet-trained-cnns-are-biased-towards","title":"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","date":"2018-11-29","arxiv_id":"1811.12231","repositories_listed":7,"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/imagenet-trained-cnns-are-biased-towards#ran","syntology_url":"https://syntology.ai/paper/1811.12231","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.12231"}},"official":{"repos":["rgeirhos/Stylized-ImageNet"],"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/espnetv2-a-light-weight-power-efficient-and","slug":"espnetv2-a-light-weight-power-efficient-and","title":"ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network","date":"2018-11-28","arxiv_id":"1811.11431","repositories_listed":10,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/espnetv2-a-light-weight-power-efficient-and#ran","syntology_url":"https://syntology.ai/paper/1811.11431","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.11431"}},"official":{"repos":["sacmehta/EdgeNets"],"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/stochastic-gradient-push-for-distributed-deep","slug":"stochastic-gradient-push-for-distributed-deep","title":"Stochastic Gradient Push for Distributed Deep Learning","date":"2018-11-27","arxiv_id":"1811.10792","repositories_listed":3,"syntology":{"n":15,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":2,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/stochastic-gradient-push-for-distributed-deep#ran","syntology_url":"https://syntology.ai/paper/1811.10792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.10792"}},"official":null}},{"url":"/paper/parametric-noise-injection-trainable","slug":"parametric-noise-injection-trainable","title":"Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack","date":"2018-11-22","arxiv_id":"1811.09310","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":3,"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/parametric-noise-injection-trainable#ran","syntology_url":"https://syntology.ai/paper/1811.09310","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.09310"}},"official":{"repos":["elliothe/CVPR_2019_PNI"],"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/gpipe-efficient-training-of-giant-neural","slug":"gpipe-efficient-training-of-giant-neural","title":"GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism","date":"2018-11-16","arxiv_id":"1811.06965","repositories_listed":13,"syntology":{"n":25,"n_ran":20,"n_constructed":0,"n_ran_checked":19,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":19,"n_pointer_only":16,"phrase":"20 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 0 violated, 19 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/gpipe-efficient-training-of-giant-neural#ran","syntology_url":"https://syntology.ai/paper/1811.06965","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.06965"}},"official":null}},{"url":"/paper/drop-activation-implicit-parameter-reduction","slug":"drop-activation-implicit-parameter-reduction","title":"Drop-Activation: Implicit Parameter Reduction and Harmonic Regularization","date":"2018-11-14","arxiv_id":"1811.05850","repositories_listed":2,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/drop-activation-implicit-parameter-reduction#ran","syntology_url":"https://syntology.ai/paper/1811.05850","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.05850"}},"official":{"repos":["LeungSamWai/Drop-Activation"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/sparsefool-a-few-pixels-make-a-big-difference","slug":"sparsefool-a-few-pixels-make-a-big-difference","title":"SparseFool: a few pixels make a big difference","date":"2018-11-06","arxiv_id":"1811.02248","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/sparsefool-a-few-pixels-make-a-big-difference#ran","syntology_url":"https://syntology.ai/paper/1811.02248","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.02248"}},"official":{"repos":["LTS4/SparseFool"],"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/uncertainty-in-neural-networks-bayesian","slug":"uncertainty-in-neural-networks-bayesian","title":"Uncertainty in Neural Networks: Approximately Bayesian Ensembling","date":"2018-10-12","arxiv_id":"1810.05546","repositories_listed":2,"syntology":{"n":16,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":9,"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) · 9 unverified","sample_list":"/paper/uncertainty-in-neural-networks-bayesian#ran","syntology_url":"https://syntology.ai/paper/1810.05546","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.05546"}},"official":{"repos":["TeaPearce/Bayesian_NN_Ensembles"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/complementary-label-learning-for-arbitrary","slug":"complementary-label-learning-for-arbitrary","title":"Complementary-Label Learning for Arbitrary Losses and Models","date":"2018-10-10","arxiv_id":"1810.04327","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/complementary-label-learning-for-arbitrary#ran","syntology_url":"https://syntology.ai/paper/1810.04327","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.04327"}},"official":{"repos":["takashiishida/comp"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-convolutional-gaussian-processes","slug":"deep-convolutional-gaussian-processes","title":"Deep convolutional Gaussian processes","date":"2018-10-06","arxiv_id":"1810.03052","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deep-convolutional-gaussian-processes#ran","syntology_url":"https://syntology.ai/paper/1810.03052","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.03052"}},"official":{"repos":["kekeblom/DeepCGP"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-compressed-transforms-with-low","slug":"learning-compressed-transforms-with-low","title":"Learning Compressed Transforms with Low Displacement Rank","date":"2018-10-04","arxiv_id":"1810.02309","repositories_listed":1,"syntology":{"n":21,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":11,"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) · 11 unverified","sample_list":"/paper/learning-compressed-transforms-with-low#ran","syntology_url":"https://syntology.ai/paper/1810.02309","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.02309"}},"official":{"repos":["HazyResearch/structured-nets"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/snip-single-shot-network-pruning-based-on","slug":"snip-single-shot-network-pruning-based-on","title":"SNIP: Single-shot Network Pruning based on Connection Sensitivity","date":"2018-10-04","arxiv_id":"1810.02340","repositories_listed":8,"syntology":{"n":12,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":7,"phrase":"5 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; 4 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/snip-single-shot-network-pruning-based-on#ran","syntology_url":"https://syntology.ai/paper/1810.02340","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.02340"}},"official":{"repos":["namhoonlee/snip-public"],"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/set-transformer-a-framework-for-attention","slug":"set-transformer-a-framework-for-attention","title":"Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks","date":"2018-10-01","arxiv_id":"1810.00825","repositories_listed":9,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":3,"n_no_contract":3,"n_pointer_only":4,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 3 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/set-transformer-a-framework-for-attention#ran","syntology_url":"https://syntology.ai/paper/1810.00825","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.00825"}},"official":{"repos":["juho-lee/set_transformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["listed","official","unlocated"]}}},{"url":"/paper/does-your-model-know-the-digit-6-is-not-a-cat","slug":"does-your-model-know-the-digit-6-is-not-a-cat","title":"A Less Biased Evaluation of Out-of-distribution Sample Detectors","date":"2018-09-13","arxiv_id":"1809.04729","repositories_listed":3,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":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) · 0 unverified","sample_list":"/paper/does-your-model-know-the-digit-6-is-not-a-cat#ran","syntology_url":"https://syntology.ai/paper/1809.04729","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.04729"}},"official":{"repos":["ashafaei/OD-test"],"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/recalibrating-fully-convolutional-networks","slug":"recalibrating-fully-convolutional-networks","title":"Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks","date":"2018-08-23","arxiv_id":"1808.08127","repositories_listed":5,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":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) · 1 unverified","sample_list":"/paper/recalibrating-fully-convolutional-networks#ran","syntology_url":"https://syntology.ai/paper/1808.08127","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.08127"}},"official":null}},{"url":"/paper/automatically-designing-cnn-architectures","slug":"automatically-designing-cnn-architectures","title":"Automatically designing CNN architectures using genetic algorithm for image classification","date":"2018-08-11","arxiv_id":"1808.03818","repositories_listed":4,"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/automatically-designing-cnn-architectures#ran","syntology_url":"https://syntology.ai/paper/1808.03818","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.03818"}},"official":{"repos":["yn-sun/cnn-ga"],"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/mnasnet-platform-aware-neural-architecture","slug":"mnasnet-platform-aware-neural-architecture","title":"MnasNet: Platform-Aware Neural Architecture Search for Mobile","date":"2018-07-31","arxiv_id":"1807.11626","repositories_listed":29,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mnasnet-platform-aware-neural-architecture#ran","syntology_url":"https://syntology.ai/paper/1807.11626","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.11626"}},"official":{"repos":["tensorflow/tpu"],"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/shufflenet-v2-practical-guidelines-for","slug":"shufflenet-v2-practical-guidelines-for","title":"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design","date":"2018-07-30","arxiv_id":"1807.11164","repositories_listed":35,"syntology":{"n":30,"n_ran":14,"n_constructed":0,"n_ran_checked":13,"n_instrument":1,"n_unverified":16,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":3,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 16 unverified","sample_list":"/paper/shufflenet-v2-practical-guidelines-for#ran","syntology_url":"https://syntology.ai/paper/1807.11164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.11164"}},"official":null}},{"url":"/paper/effects-of-degradations-on-deep-neural","slug":"effects-of-degradations-on-deep-neural","title":"Effects of Degradations on Deep Neural Network Architectures","date":"2018-07-26","arxiv_id":"1807.10108","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 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) · 3 unverified","sample_list":"/paper/effects-of-degradations-on-deep-neural#ran","syntology_url":"https://syntology.ai/paper/1807.10108","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.10108"}},"official":{"repos":["prasunroy/cnn-on-degraded-images"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/end-to-end-incremental-learning","slug":"end-to-end-incremental-learning","title":"End-to-End Incremental Learning","date":"2018-07-25","arxiv_id":"1807.09536","repositories_listed":6,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/end-to-end-incremental-learning#ran","syntology_url":"https://syntology.ai/paper/1807.09536","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.09536"}},"official":null}},{"url":"/paper/cbam-convolutional-block-attention-module","slug":"cbam-convolutional-block-attention-module","title":"CBAM: Convolutional Block Attention Module","date":"2018-07-17","arxiv_id":"1807.06521","repositories_listed":31,"syntology":{"n":22,"n_ran":13,"n_constructed":0,"n_ran_checked":10,"n_instrument":3,"n_unverified":9,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":3,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/cbam-convolutional-block-attention-module#ran","syntology_url":"https://syntology.ai/paper/1807.06521","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.06521"}},"official":null}},{"url":"/paper/invariant-information-distillation-for","slug":"invariant-information-distillation-for","title":"Invariant Information Clustering for Unsupervised Image Classification and Segmentation","date":"2018-07-17","arxiv_id":"1807.06653","repositories_listed":6,"syntology":{"n":18,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"11 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; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/invariant-information-distillation-for#ran","syntology_url":"https://syntology.ai/paper/1807.06653","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.06653"}},"official":{"repos":["xu-ji/IIC"],"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/an-intriguing-failing-of-convolutional-neural","slug":"an-intriguing-failing-of-convolutional-neural","title":"An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution","date":"2018-07-09","arxiv_id":"1807.03247","repositories_listed":24,"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":0,"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/an-intriguing-failing-of-convolutional-neural#ran","syntology_url":"https://syntology.ai/paper/1807.03247","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.03247"}},"official":{"repos":["uber-research/coordconv"],"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/pcl-proposal-cluster-learning-for-weakly","slug":"pcl-proposal-cluster-learning-for-weakly","title":"PCL: Proposal Cluster Learning for Weakly Supervised Object Detection","date":"2018-07-09","arxiv_id":"1807.03342","repositories_listed":4,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/pcl-proposal-cluster-learning-for-weakly#ran","syntology_url":"https://syntology.ai/paper/1807.03342","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.03342"}},"official":{"repos":["ppengtang/oicr"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/evaluating-feature-importance-estimates","slug":"evaluating-feature-importance-estimates","title":"A Benchmark for Interpretability Methods in Deep Neural Networks","date":"2018-06-28","arxiv_id":"1806.10758","repositories_listed":3,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/evaluating-feature-importance-estimates#ran","syntology_url":"https://syntology.ai/paper/1806.10758","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.10758"}},"official":{"repos":["google-research/google-research"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/this-looks-like-that-deep-learning-for","slug":"this-looks-like-that-deep-learning-for","title":"This Looks Like That: Deep Learning for Interpretable Image Recognition","date":"2018-06-27","arxiv_id":"1806.10574","repositories_listed":4,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/this-looks-like-that-deep-learning-for#ran","syntology_url":"https://syntology.ai/paper/1806.10574","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.10574"}},"official":{"repos":["cfchen-duke/ProtoPNet"],"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/darts-differentiable-architecture-search","slug":"darts-differentiable-architecture-search","title":"DARTS: Differentiable Architecture Search","date":"2018-06-24","arxiv_id":"1806.09055","repositories_listed":59,"syntology":{"n":156,"n_ran":74,"n_constructed":30,"n_ran_checked":60,"n_instrument":14,"n_unverified":82,"n_honours":9,"n_violates":1,"n_no_contract":50,"n_pointer_only":54,"phrase":"74 ran (of which 30 constructed an object rather than computing a result; 60 with no instrument failure: 9 honoured, 1 violated, 50 with no contract checked; 14 where Syntology's instrument failed) · 82 unverified","sample_list":"/paper/darts-differentiable-architecture-search#ran","syntology_url":"https://syntology.ai/paper/1806.09055","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.09055"}},"official":{"repos":["quark0/darts"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed"]}}},{"url":"/paper/rise-randomized-input-sampling-for","slug":"rise-randomized-input-sampling-for","title":"RISE: Randomized Input Sampling for Explanation of Black-box Models","date":"2018-06-19","arxiv_id":"1806.07421","repositories_listed":13,"syntology":{"n":34,"n_ran":32,"n_constructed":0,"n_ran_checked":32,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":31,"n_pointer_only":10,"phrase":"32 ran (of which 0 constructed an object rather than computing a result; 32 with no instrument failure: 1 honoured, 0 violated, 31 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/rise-randomized-input-sampling-for#ran","syntology_url":"https://syntology.ai/paper/1806.07421","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.07421"}},"official":{"repos":["eclique/RISE"],"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/manifold-mixup-better-representations-by","slug":"manifold-mixup-better-representations-by","title":"Manifold Mixup: Better Representations by Interpolating Hidden States","date":"2018-06-13","arxiv_id":"1806.05236","repositories_listed":12,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"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) · 4 unverified","sample_list":"/paper/manifold-mixup-better-representations-by#ran","syntology_url":"https://syntology.ai/paper/1806.05236","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.05236"}},"official":{"repos":["vikasverma1077/manifold_mixup"],"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/knowledge-distillation-by-on-the-fly-native","slug":"knowledge-distillation-by-on-the-fly-native","title":"Knowledge Distillation by On-the-Fly Native Ensemble","date":"2018-06-12","arxiv_id":"1806.04606","repositories_listed":3,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/knowledge-distillation-by-on-the-fly-native#ran","syntology_url":"https://syntology.ai/paper/1806.04606","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.04606"}},"official":null}},{"url":"/paper/bayesian-model-agnostic-meta-learning","slug":"bayesian-model-agnostic-meta-learning","title":"Bayesian Model-Agnostic Meta-Learning","date":"2018-06-11","arxiv_id":"1806.03836","repositories_listed":2,"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/bayesian-model-agnostic-meta-learning#ran","syntology_url":"https://syntology.ai/paper/1806.03836","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.03836"}},"official":null}},{"url":"/paper/path-level-network-transformation-for","slug":"path-level-network-transformation-for","title":"Path-Level Network Transformation for Efficient Architecture Search","date":"2018-06-07","arxiv_id":"1806.02639","repositories_listed":3,"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/path-level-network-transformation-for#ran","syntology_url":"https://syntology.ai/paper/1806.02639","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.02639"}},"official":{"repos":["han-cai/PathLevel-EAS"],"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/igcv3-interleaved-low-rank-group-convolutions","slug":"igcv3-interleaved-low-rank-group-convolutions","title":"IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks","date":"2018-06-01","arxiv_id":"1806.00178","repositories_listed":3,"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/igcv3-interleaved-low-rank-group-convolutions#ran","syntology_url":"https://syntology.ai/paper/1806.00178","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.00178"}},"official":{"repos":["homles11/IGCV3"],"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":["unlocated"]}}},{"url":"/paper/rotation-equivariance-and-invariance-in","slug":"rotation-equivariance-and-invariance-in","title":"Rotation Equivariance and Invariance in Convolutional Neural Networks","date":"2018-05-31","arxiv_id":"1805.12301","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/rotation-equivariance-and-invariance-in#ran","syntology_url":"https://syntology.ai/paper/1805.12301","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.12301"}},"official":{"repos":["bchidest/RiCNN"],"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/multiaccuracy-black-box-post-processing-for","slug":"multiaccuracy-black-box-post-processing-for","title":"Multiaccuracy: Black-Box Post-Processing for Fairness in Classification","date":"2018-05-31","arxiv_id":"1805.12317","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/multiaccuracy-black-box-post-processing-for#ran","syntology_url":"https://syntology.ai/paper/1805.12317","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.12317"}},"official":null}},{"url":"/paper/deep-learning-under-privileged-information","slug":"deep-learning-under-privileged-information","title":"Deep Learning under Privileged Information Using Heteroscedastic Dropout","date":"2018-05-29","arxiv_id":"1805.11614","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-learning-under-privileged-information#ran","syntology_url":"https://syntology.ai/paper/1805.11614","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.11614"}},"official":{"repos":["johnwlambert/dlupi-heteroscedastic-dropout"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/calibrating-deep-convolutional-gaussian","slug":"calibrating-deep-convolutional-gaussian","title":"Calibrating Deep Convolutional Gaussian Processes","date":"2018-05-26","arxiv_id":"1805.10522","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":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) · 3 unverified","sample_list":"/paper/calibrating-deep-convolutional-gaussian#ran","syntology_url":"https://syntology.ai/paper/1805.10522","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.10522"}},"official":null}},{"url":"/paper/autoaugment-learning-augmentation-policies","slug":"autoaugment-learning-augmentation-policies","title":"AutoAugment: Learning Augmentation Policies from Data","date":"2018-05-24","arxiv_id":"1805.09501","repositories_listed":33,"syntology":{"n":43,"n_ran":25,"n_constructed":0,"n_ran_checked":22,"n_instrument":3,"n_unverified":18,"n_honours":0,"n_violates":2,"n_no_contract":20,"n_pointer_only":4,"phrase":"25 ran (of which 0 constructed an object rather than computing a result; 22 with no instrument failure: 0 honoured, 2 violated, 20 with no contract checked; 3 where Syntology's instrument failed) · 18 unverified","sample_list":"/paper/autoaugment-learning-augmentation-policies#ran","syntology_url":"https://syntology.ai/paper/1805.09501","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.09501"}},"official":{"repos":["tensorflow/models"],"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/robust-classification-with-convolutional","slug":"robust-classification-with-convolutional","title":"Robust Classification with Convolutional Prototype Learning","date":"2018-05-09","arxiv_id":"1805.03438","repositories_listed":3,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/robust-classification-with-convolutional#ran","syntology_url":"https://syntology.ai/paper/1805.03438","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.03438"}},"official":{"repos":["YangHM/Convolutional-Prototype-Learning"],"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/co-teaching-robust-training-of-deep-neural","slug":"co-teaching-robust-training-of-deep-neural","title":"Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels","date":"2018-04-18","arxiv_id":"1804.06872","repositories_listed":5,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":1,"n_instrument":6,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":7,"phrase":"7 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; 6 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/co-teaching-robust-training-of-deep-neural#ran","syntology_url":"https://syntology.ai/paper/1804.06872","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.06872"}},"official":{"repos":["bhanML/Co-teaching"],"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/rafiki-machine-learning-as-an-analytics","slug":"rafiki-machine-learning-as-an-analytics","title":"Rafiki: Machine Learning as an Analytics Service System","date":"2018-04-17","arxiv_id":"1804.06087","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/rafiki-machine-learning-as-an-analytics#ran","syntology_url":"https://syntology.ai/paper/1804.06087","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.06087"}},"official":null}},{"url":"/paper/shapeshifter-robust-physical-adversarial","slug":"shapeshifter-robust-physical-adversarial","title":"ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector","date":"2018-04-16","arxiv_id":"1804.05810","repositories_listed":3,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"3 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/shapeshifter-robust-physical-adversarial#ran","syntology_url":"https://syntology.ai/paper/1804.05810","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.05810"}},"official":{"repos":["shangtse/robust-physical-attack"],"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/a-systematic-dnn-weight-pruning-framework","slug":"a-systematic-dnn-weight-pruning-framework","title":"A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers","date":"2018-04-10","arxiv_id":"1804.03294","repositories_listed":4,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-systematic-dnn-weight-pruning-framework#ran","syntology_url":"https://syntology.ai/paper/1804.03294","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.03294"}},"official":{"repos":["KaiqiZhang/admm-pruning"],"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/hyperdense-net-a-hyper-densely-connected-cnn","slug":"hyperdense-net-a-hyper-densely-connected-cnn","title":"HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation","date":"2018-04-09","arxiv_id":"1804.02967","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":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/hyperdense-net-a-hyper-densely-connected-cnn#ran","syntology_url":"https://syntology.ai/paper/1804.02967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.02967"}},"official":{"repos":["josedolz/HyperDenseNet"],"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/netadapt-platform-aware-neural-network","slug":"netadapt-platform-aware-neural-network","title":"NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications","date":"2018-04-09","arxiv_id":"1804.03230","repositories_listed":4,"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/netadapt-platform-aware-neural-network#ran","syntology_url":"https://syntology.ai/paper/1804.03230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.03230"}},"official":null}},{"url":"/paper/the-tsetlin-machine-a-game-theoretic-bandit","slug":"the-tsetlin-machine-a-game-theoretic-bandit","title":"The Tsetlin Machine - A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic","date":"2018-04-04","arxiv_id":"1804.01508","repositories_listed":16,"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/the-tsetlin-machine-a-game-theoretic-bandit#ran","syntology_url":"https://syntology.ai/paper/1804.01508","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.01508"}},"official":{"repos":["cair/TsetlinMachine","cair/fast-tsetlin-machine-with-mnist-demo"],"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/end-to-end-multi-task-learning-with-attention","slug":"end-to-end-multi-task-learning-with-attention","title":"End-to-End Multi-Task Learning with Attention","date":"2018-03-28","arxiv_id":"1803.10704","repositories_listed":4,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/end-to-end-multi-task-learning-with-attention#ran","syntology_url":"https://syntology.ai/paper/1803.10704","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.10704"}},"official":{"repos":["lorenmt/mtan"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/who-let-the-dogs-out-modeling-dog-behavior","slug":"who-let-the-dogs-out-modeling-dog-behavior","title":"Who Let The Dogs Out? Modeling Dog Behavior From Visual Data","date":"2018-03-28","arxiv_id":"1803.10827","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/who-let-the-dogs-out-modeling-dog-behavior#ran","syntology_url":"https://syntology.ai/paper/1803.10827","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.10827"}},"official":{"repos":["ehsanik/dogTorch"],"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/context-encoding-for-semantic-segmentation","slug":"context-encoding-for-semantic-segmentation","title":"Context Encoding for Semantic Segmentation","date":"2018-03-23","arxiv_id":"1803.08904","repositories_listed":12,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/context-encoding-for-semantic-segmentation#ran","syntology_url":"https://syntology.ai/paper/1803.08904","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.08904"}},"official":{"repos":["zhanghang1989/PyTorch-Encoding"],"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/averaging-weights-leads-to-wider-optima-and","slug":"averaging-weights-leads-to-wider-optima-and","title":"Averaging Weights Leads to Wider Optima and Better Generalization","date":"2018-03-14","arxiv_id":"1803.05407","repositories_listed":17,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":3,"n_pointer_only":4,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 1 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/averaging-weights-leads-to-wider-optima-and#ran","syntology_url":"https://syntology.ai/paper/1803.05407","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.05407"}},"official":{"repos":["timgaripov/swa"],"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/seq2sick-evaluating-the-robustness-of","slug":"seq2sick-evaluating-the-robustness-of","title":"Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples","date":"2018-03-03","arxiv_id":"1803.01128","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/seq2sick-evaluating-the-robustness-of#ran","syntology_url":"https://syntology.ai/paper/1803.01128","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.01128"}},"official":{"repos":["cmhcbb/Seq2Sick"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/directional-statistics-based-deep-metric","slug":"directional-statistics-based-deep-metric","title":"Directional Statistics-based Deep Metric Learning for Image Classification and Retrieval","date":"2018-02-27","arxiv_id":"1802.09662","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/directional-statistics-based-deep-metric#ran","syntology_url":"https://syntology.ai/paper/1802.09662","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.09662"}},"official":null}},{"url":"/paper/a-twofold-siamese-network-for-real-time","slug":"a-twofold-siamese-network-for-real-time","title":"A Twofold Siamese Network for Real-Time Object Tracking","date":"2018-02-24","arxiv_id":"1802.08817","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-twofold-siamese-network-for-real-time#ran","syntology_url":"https://syntology.ai/paper/1802.08817","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.08817"}},"official":null}},{"url":"/paper/convolutional-neural-networks-combined-with","slug":"convolutional-neural-networks-combined-with","title":"Convolutional Neural Networks combined with Runge-Kutta Methods","date":"2018-02-24","arxiv_id":"1802.08831","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":1,"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/convolutional-neural-networks-combined-with#ran","syntology_url":"https://syntology.ai/paper/1802.08831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.08831"}},"official":{"repos":["ZhuMai/RKCNN"],"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","unlocated"]}}},{"url":"/paper/recurrent-residual-convolutional-neural","slug":"recurrent-residual-convolutional-neural","title":"Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation","date":"2018-02-20","arxiv_id":"1802.06955","repositories_listed":12,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":0,"n_instrument":5,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/recurrent-residual-convolutional-neural#ran","syntology_url":"https://syntology.ai/paper/1802.06955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.06955"}},"official":null}},{"url":"/paper/model-compression-via-distillation-and","slug":"model-compression-via-distillation-and","title":"Model compression via distillation and quantization","date":"2018-02-15","arxiv_id":"1802.05668","repositories_listed":5,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"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) · 0 unverified","sample_list":"/paper/model-compression-via-distillation-and#ran","syntology_url":"https://syntology.ai/paper/1802.05668","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.05668"}},"official":{"repos":["antspy/quantized_distillation"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/encoder-decoder-with-atrous-separable","slug":"encoder-decoder-with-atrous-separable","title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","date":"2018-02-07","arxiv_id":"1802.02611","repositories_listed":78,"syntology":{"n":72,"n_ran":44,"n_constructed":17,"n_ran_checked":28,"n_instrument":16,"n_unverified":28,"n_honours":2,"n_violates":0,"n_no_contract":26,"n_pointer_only":40,"phrase":"44 ran (of which 17 constructed an object rather than computing a result; 28 with no instrument failure: 2 honoured, 0 violated, 26 with no contract checked; 16 where Syntology's instrument failed) · 28 unverified","sample_list":"/paper/encoder-decoder-with-atrous-separable#ran","syntology_url":"https://syntology.ai/paper/1802.02611","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.02611"}},"official":{"repos":["tensorflow/models"],"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/deep-convolutional-neural-networks-for-breast","slug":"deep-convolutional-neural-networks-for-breast","title":"Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis","date":"2018-02-02","arxiv_id":"1802.00752","repositories_listed":3,"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/deep-convolutional-neural-networks-for-breast#ran","syntology_url":"https://syntology.ai/paper/1802.00752","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.00752"}},"official":{"repos":["alexander-rakhlin/ICIAR2018"],"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/document-image-classification-with-intra","slug":"document-image-classification-with-intra","title":"Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks","date":"2018-01-29","arxiv_id":"1801.09321","repositories_listed":4,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":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/document-image-classification-with-intra#ran","syntology_url":"https://syntology.ai/paper/1801.09321","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1801.09321"}},"official":null}},{"url":"/paper/mobilenetv2-inverted-residuals-and-linear","slug":"mobilenetv2-inverted-residuals-and-linear","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","date":"2018-01-13","arxiv_id":"1801.04381","repositories_listed":159,"syntology":{"n":111,"n_ran":85,"n_constructed":40,"n_ran_checked":65,"n_instrument":20,"n_unverified":26,"n_honours":8,"n_violates":0,"n_no_contract":57,"n_pointer_only":64,"phrase":"85 ran (of which 40 constructed an object rather than computing a result; 65 with no instrument failure: 8 honoured, 0 violated, 57 with no contract checked; 20 where Syntology's instrument failed) · 26 unverified","sample_list":"/paper/mobilenetv2-inverted-residuals-and-linear#ran","syntology_url":"https://syntology.ai/paper/1801.04381","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1801.04381"}},"official":null}},{"url":"/paper/maximum-classifier-discrepancy-for","slug":"maximum-classifier-discrepancy-for","title":"Maximum Classifier Discrepancy for Unsupervised Domain Adaptation","date":"2017-12-07","arxiv_id":"1712.02560","repositories_listed":9,"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/maximum-classifier-discrepancy-for#ran","syntology_url":"https://syntology.ai/paper/1712.02560","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1712.02560"}},"official":{"repos":["mil-tokyo/MCD_DA"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/towards-faster-training-of-global-covariance","slug":"towards-faster-training-of-global-covariance","title":"Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization","date":"2017-12-04","arxiv_id":"1712.01034","repositories_listed":4,"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":1,"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/towards-faster-training-of-global-covariance#ran","syntology_url":"https://syntology.ai/paper/1712.01034","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1712.01034"}},"official":{"repos":["jiangtaoxie/fast-MPN-COV"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/progressive-neural-architecture-search","slug":"progressive-neural-architecture-search","title":"Progressive Neural Architecture Search","date":"2017-12-02","arxiv_id":"1712.00559","repositories_listed":18,"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":2,"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/progressive-neural-architecture-search#ran","syntology_url":"https://syntology.ai/paper/1712.00559","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1712.00559"}},"official":{"repos":["chenxi116/PNASNet.TF","tensorflow/models","chenxi116/PNASNet.pytorch"],"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"]}}}],"record_sha256":"27921302d546a819c1d4753783a1550febb55987c78a6097c0c4ab4838b6b229","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}