{"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/kaiming-initialization/papers/30","list_of":"/method/kaiming-initialization","method":"Kaiming Initialization","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":30,"pages_in_order":30,"rows_per_page":100,"rows":[2901,2931],"of":2931,"counts":{"archive_papers_tagged":2931,"with_a_code_link":1332,"where_syntology_ran_a_sample":379,"not_listed_spam_title":0,"listed":2931,"listed_where_code_ran":379,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":319,"every_run_a_failure_of_syntologys_instrument":60,"listed_with_a_run_with_no_instrument_failure":319,"listed_every_run_a_failure_of_syntologys_instrument":60,"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/kaiming-initialization","prev":"/method/kaiming-initialization/papers/29","next":null,"papers":[{"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/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":null,"slug":"semantic-segmentation-of-earth-observation","title":"Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks","date":"2016-09-22","arxiv_id":"1609.06846","n_code_links":0,"syntology":null},{"paper":"/paper/semantic-tagging-with-deep-residual-networks","slug":"semantic-tagging-with-deep-residual-networks","title":"Semantic Tagging with Deep Residual Networks","date":"2016-09-22","arxiv_id":"1609.07053","n_code_links":1,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":3,"phrase":"0 ran · 3 unverified","official":{"repos":["bjerva/semantic-tagging"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"paper":"/paper/the-microsoft-2016-conversational-speech","slug":"the-microsoft-2016-conversational-speech","title":"The Microsoft 2016 Conversational Speech Recognition System","date":"2016-09-12","arxiv_id":"1609.03528","n_code_links":0,"syntology":null},{"paper":"/paper/human-pose-estimation-via-convolutional-part","slug":"human-pose-estimation-via-convolutional-part","title":"Human pose estimation via Convolutional Part Heatmap Regression","date":"2016-09-06","arxiv_id":"1609.01743","n_code_links":1,"syntology":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/learning-structured-sparsity-in-deep-neural","slug":"learning-structured-sparsity-in-deep-neural","title":"Learning Structured Sparsity in Deep Neural Networks","date":"2016-08-12","arxiv_id":"1608.03665","n_code_links":3,"syntology":{"ran":3,"of":14,"n_ran_checked":3,"n_instrument":0,"unverified":11,"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) · 11 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/cuhk-ethz-siat-submission-to-activitynet","slug":"cuhk-ethz-siat-submission-to-activitynet","title":"CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016","date":"2016-08-02","arxiv_id":"1608.00797","n_code_links":1,"syntology":null},{"paper":"/paper/training-recurrent-answering-units-with-joint","slug":"training-recurrent-answering-units-with-joint","title":"Training Recurrent Answering Units with Joint Loss Minimization for VQA","date":"2016-06-12","arxiv_id":"1606.03647","n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-compact-bilinear-pooling-for","slug":"multimodal-compact-bilinear-pooling-for","title":"Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding","date":"2016-06-06","arxiv_id":"1606.01847","n_code_links":10,"syntology":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/hierarchical-question-image-co-attention-for","slug":"hierarchical-question-image-co-attention-for","title":"Hierarchical Question-Image Co-Attention for Visual Question Answering","date":"2016-05-31","arxiv_id":"1606.00061","n_code_links":9,"syntology":{"ran":4,"of":7,"n_ran_checked":3,"n_instrument":1,"unverified":3,"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) · 3 unverified","official":{"repos":["jiasenlu/HieCoAttenVQA"],"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/parametric-exponential-linear-unit-for-deep","slug":"parametric-exponential-linear-unit-for-deep","title":"Parametric Exponential Linear Unit for Deep Convolutional Neural Networks","date":"2016-05-30","arxiv_id":"1605.09332","n_code_links":0,"syntology":null},{"paper":"/paper/wide-residual-networks","slug":"wide-residual-networks","title":"Wide Residual Networks","date":"2016-05-23","arxiv_id":"1605.07146","n_code_links":72,"syntology":{"ran":66,"of":96,"n_ran_checked":48,"n_instrument":18,"unverified":30,"pointer_only":50,"phrase":"66 ran (of which 31 constructed an object rather than computing a result; 48 with no instrument failure: 0 honoured, 0 violated, 48 with no contract checked; 18 where Syntology's instrument failed) · 30 unverified","official":{"repos":["szagoruyko/wide-residual-networks"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":null,"slug":"deep-roots-improving-cnn-efficiency-with","title":"Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups","date":"2016-05-20","arxiv_id":"1605.06489","n_code_links":0,"syntology":null},{"paper":"/paper/r-fcn-object-detection-via-region-based-fully","slug":"r-fcn-object-detection-via-region-based-fully","title":"R-FCN: Object Detection via Region-based Fully Convolutional Networks","date":"2016-05-20","arxiv_id":"1605.06409","n_code_links":48,"syntology":{"ran":7,"of":11,"n_ran_checked":6,"n_instrument":1,"unverified":4,"pointer_only":1,"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) · 4 unverified","official":{"repos":["daijifeng001/r-fcn"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"swapout-learning-an-ensemble-of-deep","title":"Swapout: Learning an ensemble of deep architectures","date":"2016-05-20","arxiv_id":"1605.06465","n_code_links":0,"syntology":null},{"paper":"/paper/deepercut-a-deeper-stronger-and-faster-multi","slug":"deepercut-a-deeper-stronger-and-faster-multi","title":"DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model","date":"2016-05-10","arxiv_id":"1605.03170","n_code_links":16,"syntology":null},{"paper":"/paper/deep-residual-networks-with-exponential","slug":"deep-residual-networks-with-exponential","title":"Deep Residual Networks with Exponential Linear Unit","date":"2016-04-14","arxiv_id":"1604.04112","n_code_links":1,"syntology":null},{"paper":"/paper/bridging-the-gaps-between-residual-learning","slug":"bridging-the-gaps-between-residual-learning","title":"Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex","date":"2016-04-13","arxiv_id":"1604.03640","n_code_links":4,"syntology":null},{"paper":"/paper/resnet-in-resnet-generalizing-residual","slug":"resnet-in-resnet-generalizing-residual","title":"Resnet in Resnet: Generalizing Residual Architectures","date":"2016-03-25","arxiv_id":"1603.08029","n_code_links":1,"syntology":null},{"paper":"/paper/identity-mappings-in-deep-residual-networks","slug":"identity-mappings-in-deep-residual-networks","title":"Identity Mappings in Deep Residual Networks","date":"2016-03-16","arxiv_id":"1603.05027","n_code_links":54,"syntology":{"ran":14,"of":25,"n_ran_checked":12,"n_instrument":2,"unverified":11,"pointer_only":2,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 11 unverified","official":{"repos":["KaimingHe/resnet-1k-layers"],"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/hyperface-a-deep-multi-task-learning","slug":"hyperface-a-deep-multi-task-learning","title":"HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition","date":"2016-03-03","arxiv_id":"1603.01249","n_code_links":2,"syntology":null},{"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/deep-residual-learning-for-image-recognition","slug":"deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","arxiv_id":"1512.03385","n_code_links":484,"syntology":{"ran":254,"of":377,"n_ran_checked":166,"n_instrument":88,"unverified":123,"pointer_only":193,"phrase":"254 ran (of which 108 constructed an object rather than computing a result; 166 with no instrument failure: 3 honoured, 1 violated, 162 with no contract checked; 88 where Syntology's instrument failed) · 123 unverified","official":{"repos":["KaimingHe/resnet-1k-layers"],"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/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-generative-image-models-using-a-1","slug":"deep-generative-image-models-using-a-1","title":"Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks","date":"2015-06-18","arxiv_id":"1506.05751","n_code_links":1,"syntology":null},{"paper":null,"slug":"object-detection-networks-on-convolutional","title":"Object Detection Networks on Convolutional Feature Maps","date":"2015-04-23","arxiv_id":"1504.06066","n_code_links":0,"syntology":null},{"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}}],"record_sha256":"785901f9331e4efb8f1aa946a28d67613eacf7bceb2e81a69f0ad2589a5c8fd3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}