{"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":"/method/softmax/papers/ran/46","list_of":"/method/softmax","method":"Softmax","archive":{"snapshot":"2025-07-28"},"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 isolate this method inside it.","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":46,"pages_in_order":46,"rows_per_page":100,"rows":[4501,4578],"of":4578,"counts":{"archive_papers_tagged":37443,"with_a_code_link":15869,"where_syntology_ran_a_sample":4578,"not_listed_spam_title":0,"listed":37443,"listed_where_code_ran":4578,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3835,"every_run_a_failure_of_syntologys_instrument":743,"listed_with_a_run_with_no_instrument_failure":3835,"listed_every_run_a_failure_of_syntologys_instrument":743,"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":"/method/softmax/papers/ran/1","prev":"/method/softmax/papers/ran/45","next":null,"papers":[{"paper":"/paper/breaking-the-softmax-bottleneck-a-high-rank","slug":"breaking-the-softmax-bottleneck-a-high-rank","title":"Breaking the Softmax Bottleneck: A High-Rank RNN Language Model","date":"2017-11-10","arxiv_id":"1711.03953","n_code_links":9,"syntology":{"ran":12,"of":23,"n_ran_checked":11,"n_instrument":1,"unverified":11,"pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 11 unverified","official":{"repos":["zihangdai/mos"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/non-autoregressive-neural-machine-translation-1","slug":"non-autoregressive-neural-machine-translation-1","title":"Non-Autoregressive Neural Machine Translation","date":"2017-11-07","arxiv_id":"1711.02281","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":1,"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","official":{"repos":["salesforce/nonauto-nmt"],"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"]}}},{"paper":"/paper/weighted-transformer-network-for-machine","slug":"weighted-transformer-network-for-machine","title":"Weighted Transformer Network for Machine Translation","date":"2017-11-06","arxiv_id":"1711.02132","n_code_links":5,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/fine-tuning-cnn-image-retrieval-with-no-human","slug":"fine-tuning-cnn-image-retrieval-with-no-human","title":"Fine-tuning CNN Image Retrieval with No Human Annotation","date":"2017-11-03","arxiv_id":"1711.02512","n_code_links":14,"syntology":{"ran":17,"of":24,"n_ran_checked":16,"n_instrument":1,"unverified":7,"pointer_only":5,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 1 honoured, 0 violated, 15 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","official":{"repos":["filipradenovic/cnnimageretrieval-pytorch"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/deep-voice-3-scaling-text-to-speech-with","slug":"deep-voice-3-scaling-text-to-speech-with","title":"Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning","date":"2017-10-20","arxiv_id":"1710.07654","n_code_links":7,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":null}},{"paper":"/paper/coupled-ensembles-of-neural-networks","slug":"coupled-ensembles-of-neural-networks","title":"Coupled Ensembles of Neural Networks","date":"2017-09-18","arxiv_id":"1709.06053","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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","official":{"repos":["vabh/coupled_ensembles"],"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"]}}},{"paper":"/paper/simple-recurrent-units-for-highly","slug":"simple-recurrent-units-for-highly","title":"Simple Recurrent Units for Highly Parallelizable Recurrence","date":"2017-09-08","arxiv_id":"1709.02755","n_code_links":11,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["asappresearch/sru"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/squeeze-and-excitation-networks","slug":"squeeze-and-excitation-networks","title":"Squeeze-and-Excitation Networks","date":"2017-09-05","arxiv_id":"1709.01507","n_code_links":85,"syntology":{"ran":2,"of":9,"n_ran_checked":0,"n_instrument":2,"unverified":7,"pointer_only":6,"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) · 7 unverified","official":{"repos":["hujie-frank/SENet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/learned-in-translation-contextualized-word","slug":"learned-in-translation-contextualized-word","title":"Learned in Translation: Contextualized Word Vectors","date":"2017-08-01","arxiv_id":"1708.00107","n_code_links":5,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["salesforce/cove"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/dual-path-networks","slug":"dual-path-networks","title":"Dual Path Networks","date":"2017-07-06","arxiv_id":"1707.01629","n_code_links":18,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"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","official":null}},{"paper":"/paper/noisy-networks-for-exploration","slug":"noisy-networks-for-exploration","title":"Noisy Networks for Exploration","date":"2017-06-30","arxiv_id":"1706.10295","n_code_links":15,"syntology":{"ran":1,"of":3,"n_ran_checked":0,"n_instrument":1,"unverified":2,"pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/r2cnn-rotational-region-cnn-for-orientation","slug":"r2cnn-rotational-region-cnn-for-orientation","title":"R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection","date":"2017-06-29","arxiv_id":"1706.09579","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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","official":null}},{"paper":"/paper/attention-is-all-you-need","slug":"attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","arxiv_id":"1706.03762","n_code_links":595,"syntology":{"ran":610,"of":946,"n_ran_checked":529,"n_instrument":81,"unverified":336,"pointer_only":451,"phrase":"610 ran (of which 293 constructed an object rather than computing a result; 529 with no instrument failure: 45 honoured, 15 violated, 469 with no contract checked; 81 where Syntology's instrument failed) · 336 unverified","official":{"repos":["tensorflow/tensor2tensor"],"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"]}}},{"paper":"/paper/enhancing-the-reliability-of-out-of","slug":"enhancing-the-reliability-of-out-of","title":"Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks","date":"2017-06-08","arxiv_id":"1706.02690","n_code_links":9,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["facebookresearch/odin"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/sphereface-deep-hypersphere-embedding-for","slug":"sphereface-deep-hypersphere-embedding-for","title":"SphereFace: Deep Hypersphere Embedding for Face Recognition","date":"2017-04-26","arxiv_id":"1704.08063","n_code_links":22,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"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) · 1 unverified","official":{"repos":["wy1iu/sphereface"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/mobilenets-efficient-convolutional-neural","slug":"mobilenets-efficient-convolutional-neural","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","date":"2017-04-17","arxiv_id":"1704.04861","n_code_links":159,"syntology":{"ran":53,"of":83,"n_ran_checked":44,"n_instrument":9,"unverified":30,"pointer_only":48,"phrase":"53 ran (of which 28 constructed an object rather than computing a result; 44 with no instrument failure: 4 honoured, 0 violated, 40 with no contract checked; 9 where Syntology's instrument failed) · 30 unverified","official":{"repos":["tensorflow/tensorflow"],"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"]}}},{"paper":"/paper/snapshot-ensembles-train-1-get-m-for-free","slug":"snapshot-ensembles-train-1-get-m-for-free","title":"Snapshot Ensembles: Train 1, get M for free","date":"2017-04-01","arxiv_id":"1704.00109","n_code_links":11,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":{"repos":["gaohuang/SnapshotEnsemble"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/coordinating-filters-for-faster-deep-neural","slug":"coordinating-filters-for-faster-deep-neural","title":"Coordinating Filters for Faster Deep Neural Networks","date":"2017-03-28","arxiv_id":"1703.09746","n_code_links":5,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":{"repos":["wenwei202/caffe"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/arbitrary-style-transfer-in-real-time-with","slug":"arbitrary-style-transfer-in-real-time-with","title":"Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization","date":"2017-03-20","arxiv_id":"1703.06868","n_code_links":29,"syntology":{"ran":32,"of":41,"n_ran_checked":16,"n_instrument":16,"unverified":9,"pointer_only":29,"phrase":"32 ran (of which 4 constructed an object rather than computing a result; 16 with no instrument failure: 3 honoured, 0 violated, 13 with no contract checked; 16 where Syntology's instrument failed) · 9 unverified","official":{"repos":["xunhuang1995/AdaIN-style"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/mask-r-cnn","slug":"mask-r-cnn","title":"Mask R-CNN","date":"2017-03-20","arxiv_id":"1703.06870","n_code_links":179,"syntology":{"ran":101,"of":140,"n_ran_checked":90,"n_instrument":11,"unverified":39,"pointer_only":32,"phrase":"101 ran (of which 3 constructed an object rather than computing a result; 90 with no instrument failure: 0 honoured, 0 violated, 90 with no contract checked; 11 where Syntology's instrument failed) · 39 unverified","official":null}},{"paper":"/paper/model-agnostic-meta-learning-for-fast","slug":"model-agnostic-meta-learning-for-fast","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","date":"2017-03-09","arxiv_id":"1703.03400","n_code_links":85,"syntology":{"ran":103,"of":154,"n_ran_checked":72,"n_instrument":31,"unverified":51,"pointer_only":57,"phrase":"103 ran (of which 35 constructed an object rather than computing a result; 72 with no instrument failure: 6 honoured, 1 violated, 65 with no contract checked; 31 where Syntology's instrument failed) · 51 unverified","official":{"repos":["cbfinn/maml","cbfinn/maml_rl"],"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","unlocated"]}}},{"paper":"/paper/yolo9000-better-faster-stronger","slug":"yolo9000-better-faster-stronger","title":"YOLO9000: Better, Faster, Stronger","date":"2016-12-25","arxiv_id":"1612.08242","n_code_links":231,"syntology":{"ran":44,"of":60,"n_ran_checked":33,"n_instrument":11,"unverified":16,"pointer_only":23,"phrase":"44 ran (of which 0 constructed an object rather than computing a result; 33 with no instrument failure: 1 honoured, 4 violated, 28 with no contract checked; 11 where Syntology's instrument failed) · 16 unverified","official":null}},{"paper":"/paper/feature-pyramid-networks-for-object-detection","slug":"feature-pyramid-networks-for-object-detection","title":"Feature Pyramid Networks for Object Detection","date":"2016-12-09","arxiv_id":"1612.03144","n_code_links":85,"syntology":{"ran":33,"of":51,"n_ran_checked":31,"n_instrument":2,"unverified":18,"pointer_only":11,"phrase":"33 ran (of which 9 constructed an object rather than computing a result; 31 with no instrument failure: 1 honoured, 0 violated, 30 with no contract checked; 2 where Syntology's instrument failed) · 18 unverified","official":null}},{"paper":"/paper/trained-ternary-quantization","slug":"trained-ternary-quantization","title":"Trained Ternary Quantization","date":"2016-12-04","arxiv_id":"1612.01064","n_code_links":6,"syntology":{"ran":5,"of":7,"n_ran_checked":1,"n_instrument":4,"unverified":2,"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","official":null}},{"paper":"/paper/learning-python-code-suggestion-with-a-sparse","slug":"learning-python-code-suggestion-with-a-sparse","title":"Learning Python Code Suggestion with a Sparse Pointer Network","date":"2016-11-24","arxiv_id":"1611.08307","n_code_links":5,"syntology":{"ran":7,"of":9,"n_ran_checked":3,"n_instrument":4,"unverified":2,"pointer_only":0,"phrase":"7 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; 4 where Syntology's instrument failed) · 2 unverified","official":{"repos":["uclmr/pycodesuggest"],"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":["listed","official"]}}},{"paper":"/paper/multispectral-deep-neural-networks-for","slug":"multispectral-deep-neural-networks-for","title":"Multispectral Deep Neural Networks for Pedestrian Detection","date":"2016-11-08","arxiv_id":"1611.02644","n_code_links":2,"syntology":{"ran":3,"of":7,"n_ran_checked":3,"n_instrument":0,"unverified":4,"pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":null}},{"paper":"/paper/a-baseline-for-detecting-misclassified-and","slug":"a-baseline-for-detecting-misclassified-and","title":"A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks","date":"2016-10-07","arxiv_id":"1610.02136","n_code_links":14,"syntology":{"ran":19,"of":21,"n_ran_checked":16,"n_instrument":3,"unverified":2,"pointer_only":6,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 4 honoured, 0 violated, 12 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","official":{"repos":["hendrycks/error-detection"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/xception-deep-learning-with-depthwise","slug":"xception-deep-learning-with-depthwise","title":"Xception: Deep Learning with Depthwise Separable Convolutions","date":"2016-10-07","arxiv_id":"1610.02357","n_code_links":41,"syntology":{"ran":7,"of":15,"n_ran_checked":6,"n_instrument":1,"unverified":8,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 8 unverified","official":null}},{"paper":"/paper/cnn-architectures-for-large-scale-audio","slug":"cnn-architectures-for-large-scale-audio","title":"CNN Architectures for Large-Scale Audio Classification","date":"2016-09-29","arxiv_id":"1609.09430","n_code_links":16,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/pointer-sentinel-mixture-models","slug":"pointer-sentinel-mixture-models","title":"Pointer Sentinel Mixture Models","date":"2016-09-26","arxiv_id":"1609.07843","n_code_links":10,"syntology":{"ran":3,"of":9,"n_ran_checked":2,"n_instrument":1,"unverified":6,"pointer_only":1,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","official":null}},{"paper":"/paper/quantized-neural-networks-training-neural","slug":"quantized-neural-networks-training-neural","title":"Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations","date":"2016-09-22","arxiv_id":"1609.07061","n_code_links":5,"syntology":{"ran":6,"of":12,"n_ran_checked":6,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","official":null}},{"paper":"/paper/neural-photo-editing-with-introspective","slug":"neural-photo-editing-with-introspective","title":"Neural Photo Editing with Introspective Adversarial Networks","date":"2016-09-22","arxiv_id":"1609.07093","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ajbrock/Neural-Photo-Editor"],"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"]}}},{"paper":"/paper/photo-realistic-single-image-super-resolution","slug":"photo-realistic-single-image-super-resolution","title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","date":"2016-09-15","arxiv_id":"1609.04802","n_code_links":140,"syntology":{"ran":55,"of":72,"n_ran_checked":45,"n_instrument":10,"unverified":17,"pointer_only":14,"phrase":"55 ran (of which 0 constructed an object rather than computing a result; 45 with no instrument failure: 3 honoured, 3 violated, 39 with no contract checked; 10 where Syntology's instrument failed) · 17 unverified","official":null}},{"paper":"/paper/densely-connected-convolutional-networks","slug":"densely-connected-convolutional-networks","title":"Densely Connected Convolutional Networks","date":"2016-08-25","arxiv_id":"1608.06993","n_code_links":146,"syntology":{"ran":48,"of":71,"n_ran_checked":32,"n_instrument":16,"unverified":23,"pointer_only":8,"phrase":"48 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; 16 where Syntology's instrument failed) · 23 unverified","official":{"repos":["liuzhuang13/DenseNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/lets-keep-it-simple-using-simple","slug":"lets-keep-it-simple-using-simple","title":"Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures","date":"2016-08-22","arxiv_id":"1608.06037","n_code_links":9,"syntology":{"ran":11,"of":11,"n_ran_checked":8,"n_instrument":3,"unverified":0,"pointer_only":0,"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) · 0 unverified","official":{"repos":["Coderx7/SimpleNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/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","n_code_links":13,"syntology":{"ran":8,"of":13,"n_ran_checked":8,"n_instrument":0,"unverified":5,"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","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"]}}},{"paper":"/paper/infogan-interpretable-representation-learning","slug":"infogan-interpretable-representation-learning","title":"InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets","date":"2016-06-12","arxiv_id":"1606.03657","n_code_links":38,"syntology":{"ran":5,"of":6,"n_ran_checked":1,"n_instrument":4,"unverified":1,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/deeplab-semantic-image-segmentation-with-deep","slug":"deeplab-semantic-image-segmentation-with-deep","title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","date":"2016-06-02","arxiv_id":"1606.00915","n_code_links":47,"syntology":{"ran":43,"of":63,"n_ran_checked":39,"n_instrument":4,"unverified":20,"pointer_only":18,"phrase":"43 ran (of which 12 constructed an object rather than computing a result; 39 with no instrument failure: 1 honoured, 0 violated, 38 with no contract checked; 4 where Syntology's instrument failed) · 20 unverified","official":null}},{"paper":"/paper/adversarial-feature-learning","slug":"adversarial-feature-learning","title":"Adversarial Feature Learning","date":"2016-05-31","arxiv_id":"1605.09782","n_code_links":10,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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","official":null}},{"paper":"/paper/fractalnet-ultra-deep-neural-networks-without","slug":"fractalnet-ultra-deep-neural-networks-without","title":"FractalNet: Ultra-Deep Neural Networks without Residuals","date":"2016-05-24","arxiv_id":"1605.07648","n_code_links":4,"syntology":{"ran":4,"of":6,"n_ran_checked":0,"n_instrument":4,"unverified":2,"pointer_only":1,"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) · 2 unverified","official":null}},{"paper":"/paper/t-cnn-tubelets-with-convolutional-neural","slug":"t-cnn-tubelets-with-convolutional-neural","title":"T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos","date":"2016-04-09","arxiv_id":"1604.02532","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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","official":{"repos":["myfavouritekk/T-CNN"],"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"]}}},{"paper":"/paper/unsupervised-learning-of-visual-1","slug":"unsupervised-learning-of-visual-1","title":"Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles","date":"2016-03-30","arxiv_id":"1603.09246","n_code_links":9,"syntology":{"ran":10,"of":13,"n_ran_checked":7,"n_instrument":3,"unverified":3,"pointer_only":7,"phrase":"10 ran (of which 6 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":"/paper/xnor-net-imagenet-classification-using-binary","slug":"xnor-net-imagenet-classification-using-binary","title":"XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks","date":"2016-03-16","arxiv_id":"1603.05279","n_code_links":20,"syntology":{"ran":16,"of":18,"n_ran_checked":9,"n_instrument":7,"unverified":2,"pointer_only":3,"phrase":"16 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; 7 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/dynamic-memory-networks-for-visual-and","slug":"dynamic-memory-networks-for-visual-and","title":"Dynamic Memory Networks for Visual and Textual Question Answering","date":"2016-03-04","arxiv_id":"1603.01417","n_code_links":10,"syntology":{"ran":7,"of":7,"n_ran_checked":0,"n_instrument":7,"unverified":0,"pointer_only":7,"phrase":"7 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; 7 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/squeezenet-alexnet-level-accuracy-with-50x","slug":"squeezenet-alexnet-level-accuracy-with-50x","title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size","date":"2016-02-24","arxiv_id":"1602.07360","n_code_links":59,"syntology":{"ran":4,"of":4,"n_ran_checked":0,"n_instrument":4,"unverified":0,"pointer_only":2,"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","official":{"repos":["DT42/squeezenet_demo","DeepScale/SqueezeNet","Element-Research/dpnn","ejlb/squeezenet-chainer","haria/SqueezeNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/inception-v4-inception-resnet-and-the-impact","slug":"inception-v4-inception-resnet-and-the-impact","title":"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning","date":"2016-02-23","arxiv_id":"1602.07261","n_code_links":87,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":null}},{"paper":"/paper/asynchronous-methods-for-deep-reinforcement","slug":"asynchronous-methods-for-deep-reinforcement","title":"Asynchronous Methods for Deep Reinforcement Learning","date":"2016-02-04","arxiv_id":"1602.01783","n_code_links":70,"syntology":{"ran":60,"of":95,"n_ran_checked":51,"n_instrument":9,"unverified":35,"pointer_only":20,"phrase":"60 ran (of which 20 constructed an object rather than computing a result; 51 with no instrument failure: 2 honoured, 1 violated, 48 with no contract checked; 9 where Syntology's instrument failed) · 35 unverified","official":null}},{"paper":"/paper/ssd-single-shot-multibox-detector","slug":"ssd-single-shot-multibox-detector","title":"SSD: Single Shot MultiBox Detector","date":"2015-12-08","arxiv_id":"1512.02325","n_code_links":221,"syntology":{"ran":93,"of":131,"n_ran_checked":79,"n_instrument":14,"unverified":38,"pointer_only":9,"phrase":"93 ran (of which 0 constructed an object rather than computing a result; 79 with no instrument failure: 3 honoured, 1 violated, 75 with no contract checked; 14 where Syntology's instrument failed) · 38 unverified","official":{"repos":["weiliu89/caffe"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/rethinking-the-inception-architecture-for","slug":"rethinking-the-inception-architecture-for","title":"Rethinking the Inception Architecture for Computer Vision","date":"2015-12-02","arxiv_id":"1512.00567","n_code_links":113,"syntology":{"ran":20,"of":26,"n_ran_checked":19,"n_instrument":1,"unverified":6,"pointer_only":5,"phrase":"20 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 1 honoured, 0 violated, 18 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","official":null}},{"paper":"/paper/compression-of-deep-convolutional-neural","slug":"compression-of-deep-convolutional-neural","title":"Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications","date":"2015-11-20","arxiv_id":"1511.06530","n_code_links":7,"syntology":{"ran":2,"of":9,"n_ran_checked":1,"n_instrument":1,"unverified":7,"pointer_only":9,"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) · 7 unverified","official":null}},{"paper":"/paper/convolutional-neural-networks-with-low-rank","slug":"convolutional-neural-networks-with-low-rank","title":"Convolutional neural networks with low-rank regularization","date":"2015-11-19","arxiv_id":"1511.06067","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":{"repos":["chengtaipu/lowrankcnn"],"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"]}}},{"paper":"/paper/all-you-need-is-a-good-init","slug":"all-you-need-is-a-good-init","title":"All you need is a good init","date":"2015-11-19","arxiv_id":"1511.06422","n_code_links":11,"syntology":{"ran":10,"of":19,"n_ran_checked":10,"n_instrument":0,"unverified":9,"pointer_only":2,"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) · 9 unverified","official":{"repos":["ducha-aiki/LSUVinit"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"paper":"/paper/metric-learning-with-adaptive-density","slug":"metric-learning-with-adaptive-density","title":"Metric Learning with Adaptive Density Discrimination","date":"2015-11-18","arxiv_id":"1511.05939","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":null}},{"paper":"/paper/bayesian-segnet-model-uncertainty-in-deep","slug":"bayesian-segnet-model-uncertainty-in-deep","title":"Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding","date":"2015-11-09","arxiv_id":"1511.02680","n_code_links":21,"syntology":{"ran":6,"of":18,"n_ran_checked":6,"n_instrument":0,"unverified":12,"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) · 12 unverified","official":null}},{"paper":"/paper/segnet-a-deep-convolutional-encoder-decoder","slug":"segnet-a-deep-convolutional-encoder-decoder","title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","date":"2015-11-02","arxiv_id":"1511.00561","n_code_links":74,"syntology":{"ran":22,"of":44,"n_ran_checked":14,"n_instrument":8,"unverified":22,"pointer_only":11,"phrase":"22 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 1 honoured, 0 violated, 13 with no contract checked; 8 where Syntology's instrument failed) · 22 unverified","official":null}},{"paper":"/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","n_code_links":15,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"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","official":null}},{"paper":"/paper/fast-algorithms-for-convolutional-neural","slug":"fast-algorithms-for-convolutional-neural","title":"Fast Algorithms for Convolutional Neural Networks","date":"2015-09-30","arxiv_id":"1509.09308","n_code_links":5,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/tensorizing-neural-networks","slug":"tensorizing-neural-networks","title":"Tensorizing Neural Networks","date":"2015-09-22","arxiv_id":"1509.06569","n_code_links":4,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":{"repos":["Bihaqo/TensorNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/effective-approaches-to-attention-based","slug":"effective-approaches-to-attention-based","title":"Effective Approaches to Attention-based Neural Machine Translation","date":"2015-08-17","arxiv_id":"1508.04025","n_code_links":44,"syntology":{"ran":7,"of":8,"n_ran_checked":6,"n_instrument":1,"unverified":1,"pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/ask-me-anything-dynamic-memory-networks-for","slug":"ask-me-anything-dynamic-memory-networks-for","title":"Ask Me Anything: Dynamic Memory Networks for Natural Language Processing","date":"2015-06-24","arxiv_id":"1506.07285","n_code_links":10,"syntology":{"ran":5,"of":5,"n_ran_checked":4,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/pointer-networks","slug":"pointer-networks","title":"Pointer Networks","date":"2015-06-09","arxiv_id":"1506.03134","n_code_links":21,"syntology":{"ran":18,"of":23,"n_ran_checked":13,"n_instrument":5,"unverified":5,"pointer_only":9,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 3 honoured, 1 violated, 9 with no contract checked; 5 where Syntology's instrument failed) · 5 unverified","official":null}},{"paper":"/paper/learning-both-weights-and-connections-for","slug":"learning-both-weights-and-connections-for","title":"Learning both Weights and Connections for Efficient Neural Networks","date":"2015-06-08","arxiv_id":"1506.02626","n_code_links":8,"syntology":{"ran":7,"of":8,"n_ran_checked":5,"n_instrument":2,"unverified":1,"pointer_only":3,"phrase":"7 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/you-only-look-once-unified-real-time-object","slug":"you-only-look-once-unified-real-time-object","title":"You Only Look Once: Unified, Real-Time Object Detection","date":"2015-06-08","arxiv_id":"1506.02640","n_code_links":144,"syntology":{"ran":89,"of":148,"n_ran_checked":59,"n_instrument":30,"unverified":59,"pointer_only":98,"phrase":"89 ran (of which 27 constructed an object rather than computing a result; 59 with no instrument failure: 4 honoured, 4 violated, 51 with no contract checked; 30 where Syntology's instrument failed) · 59 unverified","official":null}},{"paper":"/paper/faster-r-cnn-towards-real-time-object","slug":"faster-r-cnn-towards-real-time-object","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","date":"2015-06-04","arxiv_id":"1506.01497","n_code_links":196,"syntology":{"ran":79,"of":124,"n_ran_checked":50,"n_instrument":29,"unverified":45,"pointer_only":44,"phrase":"79 ran (of which 9 constructed an object rather than computing a result; 50 with no instrument failure: 15 honoured, 5 violated, 30 with no contract checked; 29 where Syntology's instrument failed) · 45 unverified","official":{"repos":["ShaoqingRen/faster_rcnn","rbgirshick/py-faster-rcnn"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/cyclical-learning-rates-for-training-neural","slug":"cyclical-learning-rates-for-training-neural","title":"Cyclical Learning Rates for Training Neural Networks","date":"2015-06-03","arxiv_id":"1506.01186","n_code_links":53,"syntology":{"ran":15,"of":17,"n_ran_checked":12,"n_instrument":3,"unverified":2,"pointer_only":2,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","official":{"repos":["bckenstler/CLR"],"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"]}}},{"paper":"/paper/learning-deconvolution-network-for-semantic","slug":"learning-deconvolution-network-for-semantic","title":"Learning Deconvolution Network for Semantic Segmentation","date":"2015-05-17","arxiv_id":"1505.04366","n_code_links":5,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"pointer_only":2,"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) · 1 unverified","official":null}},{"paper":"/paper/fast-r-cnn","slug":"fast-r-cnn","title":"Fast R-CNN","date":"2015-04-30","arxiv_id":"1504.08083","n_code_links":30,"syntology":{"ran":5,"of":8,"n_ran_checked":2,"n_instrument":3,"unverified":3,"pointer_only":1,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","official":{"repos":["rbgirshick/fast-rcnn"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/classifying-relations-by-ranking-with","slug":"classifying-relations-by-ranking-with","title":"Classifying Relations by Ranking with Convolutional Neural Networks","date":"2015-04-24","arxiv_id":"1504.06580","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":null}},{"paper":"/paper/end-to-end-memory-networks","slug":"end-to-end-memory-networks","title":"End-To-End Memory Networks","date":"2015-03-31","arxiv_id":"1503.08895","n_code_links":44,"syntology":{"ran":4,"of":15,"n_ran_checked":3,"n_instrument":1,"unverified":11,"pointer_only":5,"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) · 11 unverified","official":{"repos":["facebook/MemNN"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/batch-normalization-accelerating-deep-network","slug":"batch-normalization-accelerating-deep-network","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","date":"2015-02-11","arxiv_id":"1502.03167","n_code_links":70,"syntology":{"ran":19,"of":21,"n_ran_checked":14,"n_instrument":5,"unverified":2,"pointer_only":5,"phrase":"19 ran (of which 5 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/conditional-random-fields-as-recurrent-neural-1","slug":"conditional-random-fields-as-recurrent-neural-1","title":"Conditional Random Fields as Recurrent Neural Networks","date":"2015-02-11","arxiv_id":"1502.03240","n_code_links":6,"syntology":{"ran":7,"of":8,"n_ran_checked":7,"n_instrument":0,"unverified":1,"pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["torrvision/crfasrnn"],"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"]}}},{"paper":"/paper/delving-deep-into-rectifiers-surpassing-human","slug":"delving-deep-into-rectifiers-surpassing-human","title":"Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification","date":"2015-02-06","arxiv_id":"1502.01852","n_code_links":15,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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","official":null}},{"paper":"/paper/fully-convolutional-networks-for-semantic-1","slug":"fully-convolutional-networks-for-semantic-1","title":"Fully Convolutional Networks for Semantic Segmentation","date":"2014-11-14","arxiv_id":"1411.4038","n_code_links":51,"syntology":{"ran":3,"of":4,"n_ran_checked":0,"n_instrument":3,"unverified":1,"pointer_only":4,"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) · 1 unverified","official":null}},{"paper":"/paper/word2vec-parameter-learning-explained","slug":"word2vec-parameter-learning-explained","title":"word2vec Parameter Learning Explained","date":"2014-11-11","arxiv_id":"1411.2738","n_code_links":8,"syntology":{"ran":9,"of":9,"n_ran_checked":7,"n_instrument":2,"unverified":0,"pointer_only":5,"phrase":"9 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ronxin/wevi"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/going-deeper-with-convolutions","slug":"going-deeper-with-convolutions","title":"Going Deeper with Convolutions","date":"2014-09-17","arxiv_id":"1409.4842","n_code_links":83,"syntology":{"ran":34,"of":42,"n_ran_checked":30,"n_instrument":4,"unverified":8,"pointer_only":21,"phrase":"34 ran (of which 19 constructed an object rather than computing a result; 30 with no instrument failure: 0 honoured, 0 violated, 30 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","official":{"repos":["worksheets.codalab.org/worksheets/0xbcd424d2bf544c4786efcc0063759b1a"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/very-deep-convolutional-networks-for-large","slug":"very-deep-convolutional-networks-for-large","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","date":"2014-09-04","arxiv_id":"1409.1556","n_code_links":305,"syntology":{"ran":81,"of":122,"n_ran_checked":71,"n_instrument":10,"unverified":41,"pointer_only":8,"phrase":"81 ran (of which 0 constructed an object rather than computing a result; 71 with no instrument failure: 0 honoured, 0 violated, 71 with no contract checked; 10 where Syntology's instrument failed) · 41 unverified","official":null}},{"paper":"/paper/visualizing-and-understanding-convolutional","slug":"visualizing-and-understanding-convolutional","title":"Visualizing and Understanding Convolutional Networks","date":"2013-11-12","arxiv_id":"1311.2901","n_code_links":18,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":3,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/distributed-representations-of-words-and-1","slug":"distributed-representations-of-words-and-1","title":"Distributed Representations of Words and Phrases and their Compositionality","date":"2013-10-16","arxiv_id":"1310.4546","n_code_links":51,"syntology":{"ran":21,"of":26,"n_ran_checked":20,"n_instrument":1,"unverified":5,"pointer_only":4,"phrase":"21 ran (of which 2 constructed an object rather than computing a result; 20 with no instrument failure: 0 honoured, 0 violated, 20 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","official":null}}],"record_sha256":"eb7c8228339018d51f99e445d9f7efe91b35e01ea5e926973fb68673bea335a2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}