{"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/quantization/papers/ran/6","list_of":"/task/quantization","task":"Quantization","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":6,"pages_in_order":6,"rows_per_page":100,"rows":[501,515],"of":515,"counts":{"archive_papers_tagged":4925,"with_a_code_link":1596,"where_syntology_ran_a_sample":515,"not_listed_spam_title":0,"listed":4925,"listed_where_code_ran":515,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":452,"every_run_a_failure_of_syntologys_instrument":63,"listed_with_a_run_with_no_instrument_failure":452,"listed_every_run_a_failure_of_syntologys_instrument":63,"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/quantization/papers/ran/1","prev":"/task/quantization/papers/ran/5","next":null,"papers":[{"url":"/paper/rgcnn-regularized-graph-cnn-for-point-cloud","slug":"rgcnn-regularized-graph-cnn-for-point-cloud","title":"RGCNN: Regularized Graph CNN for Point Cloud Segmentation","date":"2018-06-08","arxiv_id":"1806.02952","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/rgcnn-regularized-graph-cnn-for-point-cloud#ran","syntology_url":"https://syntology.ai/paper/1806.02952","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.02952"}},"official":null}},{"url":"/paper/spreading-vectors-for-similarity-search","slug":"spreading-vectors-for-similarity-search","title":"Spreading vectors for similarity search","date":"2018-06-08","arxiv_id":"1806.03198","repositories_listed":2,"syntology":{"n":15,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":10,"n_honours":1,"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; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/spreading-vectors-for-similarity-search#ran","syntology_url":"https://syntology.ai/paper/1806.03198","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.03198"}},"official":{"repos":["facebookresearch/spreadingvectors"],"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/scalable-methods-for-8-bit-training-of-neural","slug":"scalable-methods-for-8-bit-training-of-neural","title":"Scalable Methods for 8-bit Training of Neural Networks","date":"2018-05-25","arxiv_id":"1805.11046","repositories_listed":3,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"8 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; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/scalable-methods-for-8-bit-training-of-neural#ran","syntology_url":"https://syntology.ai/paper/1805.11046","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.11046"}},"official":{"repos":["eladhoffer/quantized.pytorch"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/feature-distillation-dnn-oriented-jpeg","slug":"feature-distillation-dnn-oriented-jpeg","title":"Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples","date":"2018-03-14","arxiv_id":"1803.05787","repositories_listed":2,"syntology":{"n":17,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":5,"n_honours":3,"n_violates":0,"n_no_contract":9,"n_pointer_only":3,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 3 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/feature-distillation-dnn-oriented-jpeg#ran","syntology_url":"https://syntology.ai/paper/1803.05787","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.05787"}},"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/binaryrelax-a-relaxation-approach-for","slug":"binaryrelax-a-relaxation-approach-for","title":"BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized Weights","date":"2018-01-19","arxiv_id":"1801.06313","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":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/binaryrelax-a-relaxation-approach-for#ran","syntology_url":"https://syntology.ai/paper/1801.06313","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1801.06313"}},"official":null}},{"url":"/paper/quantization-and-training-of-neural-networks","slug":"quantization-and-training-of-neural-networks","title":"Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference","date":"2017-12-15","arxiv_id":"1712.05877","repositories_listed":21,"syntology":{"n":7,"n_ran":7,"n_constructed":3,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"7 ran (of which 3 constructed an object rather than computing a result; 6 with no instrument failure: 3 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/quantization-and-training-of-neural-networks#ran","syntology_url":"https://syntology.ai/paper/1712.05877","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1712.05877"}},"official":null}},{"url":"/paper/attacking-binarized-neural-networks","slug":"attacking-binarized-neural-networks","title":"Attacking Binarized Neural Networks","date":"2017-11-01","arxiv_id":"1711.00449","repositories_listed":1,"syntology":{"n":19,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":14,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":19,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 14 unverified","sample_list":"/paper/attacking-binarized-neural-networks#ran","syntology_url":"https://syntology.ai/paper/1711.00449","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.00449"}},"official":{"repos":["AngusG/cleverhans-attacking-bnns"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":14,"ran_from_kinds":["official"]}}},{"url":"/paper/billion-scale-similarity-search-with-gpus","slug":"billion-scale-similarity-search-with-gpus","title":"Billion-scale similarity search with GPUs","date":"2017-02-28","arxiv_id":"1702.08734","repositories_listed":14,"syntology":{"n":35,"n_ran":16,"n_constructed":1,"n_ran_checked":11,"n_instrument":5,"n_unverified":19,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":27,"phrase":"16 ran (of which 1 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 5 where Syntology's instrument failed) · 19 unverified","sample_list":"/paper/billion-scale-similarity-search-with-gpus#ran","syntology_url":"https://syntology.ai/paper/1702.08734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1702.08734"}},"official":{"repos":["facebookresearch/faiss"],"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/efficient-large-scale-approximate-nearest","slug":"efficient-large-scale-approximate-nearest","title":"Efficient Large-scale Approximate Nearest Neighbor Search on the GPU","date":"2017-02-20","arxiv_id":"1702.05911","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficient-large-scale-approximate-nearest#ran","syntology_url":"https://syntology.ai/paper/1702.05911","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1702.05911"}},"official":null}},{"url":"/paper/fasttextzip-compressing-text-classification","slug":"fasttextzip-compressing-text-classification","title":"FastText.zip: Compressing text classification models","date":"2016-12-12","arxiv_id":"1612.03651","repositories_listed":44,"syntology":{"n":18,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":13,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/fasttextzip-compressing-text-classification#ran","syntology_url":"https://syntology.ai/paper/1612.03651","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.03651"}},"official":{"repos":["facebookresearch/fastText"],"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/trained-ternary-quantization","slug":"trained-ternary-quantization","title":"Trained Ternary Quantization","date":"2016-12-04","arxiv_id":"1612.01064","repositories_listed":6,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/trained-ternary-quantization#ran","syntology_url":"https://syntology.ai/paper/1612.01064","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.01064"}},"official":null}},{"url":"/paper/polysemous-codes","slug":"polysemous-codes","title":"Polysemous codes","date":"2016-09-07","arxiv_id":"1609.01882","repositories_listed":11,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":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) · 1 unverified","sample_list":"/paper/polysemous-codes#ran","syntology_url":"https://syntology.ai/paper/1609.01882","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1609.01882"}},"official":null}},{"url":"/paper/dorefa-net-training-low-bitwidth","slug":"dorefa-net-training-low-bitwidth","title":"DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients","date":"2016-06-20","arxiv_id":"1606.06160","repositories_listed":13,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/dorefa-net-training-low-bitwidth#ran","syntology_url":"https://syntology.ai/paper/1606.06160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.06160"}},"official":{"repos":["tensorpack/tensorpack"],"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-compression-compressing-deep-neural","slug":"deep-compression-compressing-deep-neural","title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","date":"2015-10-01","arxiv_id":"1510.00149","repositories_listed":15,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"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) · 2 unverified","sample_list":"/paper/deep-compression-compressing-deep-neural#ran","syntology_url":"https://syntology.ai/paper/1510.00149","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1510.00149"}},"official":null}}],"record_sha256":"06f9413802b4e418cf437f6cbf361b1112e419e28a12ce827f14b6b76a73a31f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}