{"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/relu/papers/94","list_of":"/method/relu","method":"ReLU","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":94,"pages_in_order":104,"rows_per_page":100,"rows":[9301,9400],"of":10350,"counts":{"archive_papers_tagged":10350,"with_a_code_link":4256,"where_syntology_ran_a_sample":1079,"not_listed_spam_title":0,"listed":10350,"listed_where_code_ran":1079,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":909,"every_run_a_failure_of_syntologys_instrument":170,"listed_with_a_run_with_no_instrument_failure":909,"listed_every_run_a_failure_of_syntologys_instrument":170,"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/relu","prev":"/method/relu/papers/93","next":"/method/relu/papers/95","papers":[{"paper":null,"slug":"improved-breast-mass-segmentation-in","title":"Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net","date":"2018-08-27","arxiv_id":"1808.08885","n_code_links":0,"syntology":null},{"paper":"/paper/wide-activation-for-efficient-and-accurate","slug":"wide-activation-for-efficient-and-accurate","title":"Wide Activation for Efficient and Accurate Image Super-Resolution","date":"2018-08-27","arxiv_id":"1808.08718","n_code_links":12,"syntology":{"ran":9,"of":17,"n_ran_checked":9,"n_instrument":0,"unverified":8,"pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","official":{"repos":["JiahuiYu/wdsr_ntire2018"],"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/semi-autoregressive-neural-machine","slug":"semi-autoregressive-neural-machine","title":"Semi-Autoregressive Neural Machine Translation","date":"2018-08-26","arxiv_id":"1808.08583","n_code_links":1,"syntology":{"ran":1,"of":4,"n_ran_checked":1,"n_instrument":0,"unverified":3,"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","official":{"repos":["chqiwang/sa-nmt"],"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"]}}},{"paper":null,"slug":"spectral-pruning-compressing-deep-neural","title":"Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error","date":"2018-08-26","arxiv_id":"1808.08558","n_code_links":0,"syntology":null},{"paper":null,"slug":"how-many-labeled-license-plates-are-needed","title":"How many labeled license plates are needed?","date":"2018-08-25","arxiv_id":"1808.08410","n_code_links":0,"syntology":null},{"paper":null,"slug":"organ-at-risk-segmentation-in-head-and-neck","title":"Organ at Risk Segmentation in Head and Neck CT Images by Using a Two-Stage Segmentation Framework Based on 3D U-Net","date":"2018-08-25","arxiv_id":"1809.00960","n_code_links":0,"syntology":null},{"paper":null,"slug":"paranet-using-dense-blocks-for-early","title":"ParaNet - Using Dense Blocks for Early Inference","date":"2018-08-24","arxiv_id":"1808.08308","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":5,"syntology":{"ran":7,"of":8,"n_ran_checked":5,"n_instrument":2,"unverified":1,"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","official":null}},{"paper":"/paper/an-attention-gated-convolutional-neural","slug":"an-attention-gated-convolutional-neural","title":"An Attention-Gated Convolutional Neural Network for Sentence Classification","date":"2018-08-22","arxiv_id":"1808.07325","n_code_links":2,"syntology":null},{"paper":"/paper/attention-gated-networks-learning-to-leverage","slug":"attention-gated-networks-learning-to-leverage","title":"Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images","date":"2018-08-22","arxiv_id":"1808.08114","n_code_links":2,"syntology":null},{"paper":null,"slug":"rethinking-monocular-depth-estimation-with","title":"Rethinking Monocular Depth Estimation with Adversarial Training","date":"2018-08-22","arxiv_id":"1808.07528","n_code_links":0,"syntology":null},{"paper":"/paper/training-deeper-neural-machine-translation","slug":"training-deeper-neural-machine-translation","title":"Training Deeper Neural Machine Translation Models with Transparent Attention","date":"2018-08-22","arxiv_id":"1808.07561","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-to-exploit-invariances-in-clinical","title":"Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks","date":"2018-08-21","arxiv_id":"1808.06725","n_code_links":0,"syntology":null},{"paper":"/paper/capsdemm-capsule-network-for-detection-of","slug":"capsdemm-capsule-network-for-detection-of","title":"CapsDeMM: Capsule network for Detection of Munro's Microabscess in skin biopsy images","date":"2018-08-20","arxiv_id":"1808.06428","n_code_links":1,"syntology":null},{"paper":"/paper/cu-net-coupled-u-nets","slug":"cu-net-coupled-u-nets","title":"CU-Net: Coupled U-Nets","date":"2018-08-20","arxiv_id":"1808.06521","n_code_links":1,"syntology":null},{"paper":null,"slug":"veram-view-enhanced-recurrent-attention-model","title":"VERAM: View-Enhanced Recurrent Attention Model for 3D Shape Classification","date":"2018-08-20","arxiv_id":"1808.06698","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-mask-for-x-ray-based-heart-disease","title":"Deep Mask For X-ray Based Heart Disease Classification","date":"2018-08-19","arxiv_id":"1808.08277","n_code_links":0,"syntology":null},{"paper":null,"slug":"epithelium-segmentation-using-deep-learning","title":"Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard","date":"2018-08-17","arxiv_id":"1808.05883","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-quantize-deep-networks-by","title":"Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss","date":"2018-08-17","arxiv_id":"1808.05779","n_code_links":0,"syntology":null},{"paper":"/paper/deep-convolutional-networks-as-shallow","slug":"deep-convolutional-networks-as-shallow","title":"Deep Convolutional Networks as shallow Gaussian Processes","date":"2018-08-16","arxiv_id":"1808.05587","n_code_links":3,"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":["convnets-as-gps/convnets-as-gps","rhaps0dy/convnets-as-gps"],"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":null,"slug":"deep-learning-for-energy-markets","title":"Deep Learning for Energy Markets","date":"2018-08-16","arxiv_id":"1808.05527","n_code_links":0,"syntology":null},{"paper":"/paper/larnn-linear-attention-recurrent-neural","slug":"larnn-linear-attention-recurrent-neural","title":"LARNN: Linear Attention Recurrent Neural Network","date":"2018-08-16","arxiv_id":"1808.05578","n_code_links":1,"syntology":null},{"paper":"/paper/network-decoupling-from-regular-to-depthwise","slug":"network-decoupling-from-regular-to-depthwise","title":"Network Decoupling: From Regular to Depthwise Separable Convolutions","date":"2018-08-16","arxiv_id":"1808.05517","n_code_links":1,"syntology":null},{"paper":"/paper/a-simple-convolutional-generative-network-for","slug":"a-simple-convolutional-generative-network-for","title":"A Simple Convolutional Generative Network for Next Item Recommendation","date":"2018-08-15","arxiv_id":"1808.05163","n_code_links":3,"syntology":null},{"paper":"/paper/anatomynet-deep-learning-for-fast-and-fully","slug":"anatomynet-deep-learning-for-fast-and-fully","title":"AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy","date":"2018-08-15","arxiv_id":"1808.05238","n_code_links":2,"syntology":null},{"paper":"/paper/dnn-feature-map-compression-using-learned","slug":"dnn-feature-map-compression-using-learned","title":"DNN Feature Map Compression using Learned Representation over GF(2)","date":"2018-08-15","arxiv_id":"1808.05285","n_code_links":1,"syntology":null},{"paper":null,"slug":"cache-telepathy-leveraging-shared-resource","title":"Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN Architectures","date":"2018-08-14","arxiv_id":"1808.04761","n_code_links":0,"syntology":null},{"paper":"/paper/gesturegan-for-hand-gesture-to-gesture","slug":"gesturegan-for-hand-gesture-to-gesture","title":"GestureGAN for Hand Gesture-to-Gesture Translation in the Wild","date":"2018-08-14","arxiv_id":"1808.04859","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-relu-networks-on-linearly-separable","title":"Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization","date":"2018-08-14","arxiv_id":"1808.04685","n_code_links":0,"syntology":null},{"paper":null,"slug":"scargan-chained-generative-adversarial","title":"ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans","date":"2018-08-14","arxiv_id":"1808.04500","n_code_links":0,"syntology":null},{"paper":null,"slug":"denseran-for-offline-handwritten-chinese","title":"DenseRAN for Offline Handwritten Chinese Character Recognition","date":"2018-08-13","arxiv_id":"1808.04134","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-learning-for-cross-domain","title":"Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks","date":"2018-08-12","arxiv_id":"1808.03944","n_code_links":0,"syntology":null},{"paper":null,"slug":"fully-automated-analysis-of-body-composition","title":"Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks","date":"2018-08-11","arxiv_id":"1808.03844","n_code_links":0,"syntology":null},{"paper":null,"slug":"densely-connected-convolutional-networks-for","title":"Densely Connected Convolutional Networks for Speech Recognition","date":"2018-08-10","arxiv_id":"1808.03570","n_code_links":0,"syntology":null},{"paper":"/paper/weakly-and-semi-supervised-panoptic","slug":"weakly-and-semi-supervised-panoptic","title":"Weakly- and Semi-Supervised Panoptic Segmentation","date":"2018-08-10","arxiv_id":"1808.03575","n_code_links":1,"syntology":null},{"paper":"/paper/character-level-language-modeling-with-deeper","slug":"character-level-language-modeling-with-deeper","title":"Character-Level Language Modeling with Deeper Self-Attention","date":"2018-08-09","arxiv_id":"1808.04444","n_code_links":1,"syntology":null},{"paper":"/paper/design-challenges-in-named-entity","slug":"design-challenges-in-named-entity","title":"Design Challenges in Named Entity Transliteration","date":"2018-08-07","arxiv_id":"1808.02563","n_code_links":1,"syntology":null},{"paper":null,"slug":"rethinking-numerical-representations-for-deep","title":"Rethinking Numerical Representations for Deep Neural Networks","date":"2018-08-07","arxiv_id":"1808.02513","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-estimator-full-left-ventricle","title":"Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning","date":"2018-08-06","arxiv_id":"1808.02056","n_code_links":0,"syntology":null},{"paper":null,"slug":"x-gans-image-reconstruction-made-easy-for","title":"X-GANs: Image Reconstruction Made Easy for Extreme Cases","date":"2018-08-06","arxiv_id":"1808.04432","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-multi-task-framework-for-skin-lesion","title":"A Multi-task Framework for Skin Lesion Detection and Segmentation","date":"2018-08-05","arxiv_id":"1808.01676","n_code_links":0,"syntology":null},{"paper":null,"slug":"dilated-convolutions-in-neural-networks-for","title":"Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI","date":"2018-08-05","arxiv_id":"1808.01673","n_code_links":0,"syntology":null},{"paper":"/paper/is-robustness-the-cost-of-accuracy-a","slug":"is-robustness-the-cost-of-accuracy-a","title":"Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models","date":"2018-08-05","arxiv_id":"1808.01688","n_code_links":2,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"0 ran · 2 unverified","official":{"repos":["huanzhang12/Adversarial_Survey"],"state":"official: not harvested","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"paper":"/paper/skin-lesion-diagnosis-using-ensembles","slug":"skin-lesion-diagnosis-using-ensembles","title":"Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weighting","date":"2018-08-05","arxiv_id":"1808.01694","n_code_links":2,"syntology":null},{"paper":null,"slug":"on-lipschitz-bounds-of-general-convolutional","title":"On Lipschitz Bounds of General Convolutional Neural Networks","date":"2018-08-04","arxiv_id":"1808.01415","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-expressive-speaking-style-from","title":"Predicting Expressive Speaking Style From Text In End-To-End Speech Synthesis","date":"2018-08-04","arxiv_id":"1808.01410","n_code_links":0,"syntology":null},{"paper":"/paper/cornernet-detecting-objects-as-paired","slug":"cornernet-detecting-objects-as-paired","title":"CornerNet: Detecting Objects as Paired Keypoints","date":"2018-08-03","arxiv_id":"1808.01244","n_code_links":5,"syntology":{"ran":3,"of":11,"n_ran_checked":1,"n_instrument":2,"unverified":8,"pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 8 unverified","official":{"repos":["princeton-vl/CornerNet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":8,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"learning-overparameterized-neural-networks","title":"Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data","date":"2018-08-03","arxiv_id":"1808.01204","n_code_links":0,"syntology":null},{"paper":"/paper/bisenet-bilateral-segmentation-network-for","slug":"bisenet-bilateral-segmentation-network-for","title":"BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation","date":"2018-08-02","arxiv_id":"1808.00897","n_code_links":21,"syntology":{"ran":14,"of":18,"n_ran_checked":9,"n_instrument":5,"unverified":4,"pointer_only":3,"phrase":"14 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; 5 where Syntology's instrument failed) · 4 unverified","official":null}},{"paper":"/paper/diverse-image-to-image-translation-via","slug":"diverse-image-to-image-translation-via","title":"Diverse Image-to-Image Translation via Disentangled Representations","date":"2018-08-02","arxiv_id":"1808.00948","n_code_links":7,"syntology":null},{"paper":"/paper/bi-real-net-enhancing-the-performance-of-1","slug":"bi-real-net-enhancing-the-performance-of-1","title":"Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm","date":"2018-08-01","arxiv_id":"1808.00278","n_code_links":4,"syntology":{"ran":4,"of":4,"n_ran_checked":0,"n_instrument":4,"unverified":0,"pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","official":{"repos":["liuzechun/Bi-Real-net"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":"/paper/instance-level-human-parsing-via-part","slug":"instance-level-human-parsing-via-part","title":"Instance-level Human Parsing via Part Grouping Network","date":"2018-08-01","arxiv_id":"1808.00157","n_code_links":1,"syntology":{"ran":6,"of":14,"n_ran_checked":6,"n_instrument":0,"unverified":8,"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) · 8 unverified","official":{"repos":["Engineering-Course/CIHP_PGN"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":8,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"neural-machine-translation-with-decoding","title":"Neural Machine Translation with Decoding History Enhanced Attention","date":"2018-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/slimnets-an-exploration-of-deep-model","slug":"slimnets-an-exploration-of-deep-model","title":"SlimNets: An Exploration of Deep Model Compression and Acceleration","date":"2018-08-01","arxiv_id":"1808.00496","n_code_links":1,"syntology":null},{"paper":"/paper/cutting-down-training-memory-by-re-fowarding","slug":"cutting-down-training-memory-by-re-fowarding","title":"Optimal Gradient Checkpoint Search for Arbitrary Computation Graphs","date":"2018-07-31","arxiv_id":"1808.00079","n_code_links":1,"syntology":null},{"paper":"/paper/deep-end-to-end-fingerprint-denoising-and","slug":"deep-end-to-end-fingerprint-denoising-and","title":"Deep End-to-end Fingerprint Denoising and Inpainting","date":"2018-07-31","arxiv_id":"1807.11888","n_code_links":1,"syntology":null},{"paper":"/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","n_code_links":29,"syntology":{"ran":4,"of":6,"n_ran_checked":3,"n_instrument":1,"unverified":2,"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","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"]}}},{"paper":null,"slug":"spectrum-concentration-in-deep-residual","title":"Spectrum concentration in deep residual learning: a free probability approach","date":"2018-07-31","arxiv_id":"1807.11694","n_code_links":0,"syntology":null},{"paper":"/paper/acquisition-of-localization-confidence-for","slug":"acquisition-of-localization-confidence-for","title":"Acquisition of Localization Confidence for Accurate Object Detection","date":"2018-07-30","arxiv_id":"1807.11590","n_code_links":4,"syntology":{"ran":17,"of":18,"n_ran_checked":15,"n_instrument":2,"unverified":1,"pointer_only":2,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["vacancy/PreciseRoIPooling"],"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":"doubly-attentive-transformer-machine","title":"Doubly Attentive Transformer Machine Translation","date":"2018-07-30","arxiv_id":"1807.11605","n_code_links":0,"syntology":null},{"paper":null,"slug":"extreme-network-compression-via-filter-group","title":"Extreme Network Compression via Filter Group Approximation","date":"2018-07-30","arxiv_id":"1807.11254","n_code_links":0,"syntology":null},{"paper":null,"slug":"highly-scalable-deep-learning-training-system","title":"Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes","date":"2018-07-30","arxiv_id":"1807.11205","n_code_links":0,"syntology":null},{"paper":"/paper/improving-electron-micrograph-signal-to-noise","slug":"improving-electron-micrograph-signal-to-noise","title":"Improving Electron Micrograph Signal-to-Noise with an Atrous Convolutional Encoder-Decoder","date":"2018-07-30","arxiv_id":"1807.11234","n_code_links":1,"syntology":null},{"paper":"/paper/multi-fiber-networks-for-video-recognition","slug":"multi-fiber-networks-for-video-recognition","title":"Multi-Fiber Networks for Video Recognition","date":"2018-07-30","arxiv_id":"1807.11195","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":35,"syntology":{"ran":14,"of":30,"n_ran_checked":13,"n_instrument":1,"unverified":16,"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","official":null}},{"paper":"/paper/adam-admm-a-unified-systematic-framework-of","slug":"adam-admm-a-unified-systematic-framework-of","title":"StructADMM: A Systematic, High-Efficiency Framework of Structured Weight Pruning for DNNs","date":"2018-07-29","arxiv_id":"1807.11091","n_code_links":1,"syntology":null},{"paper":"/paper/reenactgan-learning-to-reenact-faces-via","slug":"reenactgan-learning-to-reenact-faces-via","title":"ReenactGAN: Learning to Reenact Faces via Boundary Transfer","date":"2018-07-29","arxiv_id":"1807.11079","n_code_links":1,"syntology":null},{"paper":null,"slug":"maskconnect-connectivity-learning-by-gradient","title":"MaskConnect: Connectivity Learning by Gradient Descent","date":"2018-07-28","arxiv_id":"1807.11473","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-unified-approximation-framework-for-deep","title":"A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks","date":"2018-07-26","arxiv_id":"1807.10119","n_code_links":0,"syntology":null},{"paper":null,"slug":"deepspine-automated-lumbar-vertebral","title":"DeepSPINE: Automated Lumbar Vertebral Segmentation, Disc-level Designation, and Spinal Stenosis Grading Using Deep Learning","date":"2018-07-26","arxiv_id":"1807.10215","n_code_links":0,"syntology":null},{"paper":"/paper/effectiveness-of-scaled-exponentially","slug":"effectiveness-of-scaled-exponentially","title":"Effectiveness of Scaled Exponentially-Regularized Linear Units (SERLUs)","date":"2018-07-26","arxiv_id":"1807.10117","n_code_links":0,"syntology":null},{"paper":null,"slug":"false-positive-reduction-by-actively-mining","title":"False Positive Reduction by Actively Mining Negative Samples for Pulmonary Nodule Detection in Chest Radiographs","date":"2018-07-26","arxiv_id":"1807.10756","n_code_links":0,"syntology":null},{"paper":"/paper/lq-nets-learned-quantization-for-highly","slug":"lq-nets-learned-quantization-for-highly","title":"LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks","date":"2018-07-26","arxiv_id":"1807.10029","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":["Microsoft/LQ-Nets"],"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":null,"slug":"mri-to-fdg-pet-cross-modal-synthesis-using-3d","title":"MRI to FDG-PET: Cross-Modal Synthesis Using 3D U-Net For Multi-Modal Alzheimer's Classification","date":"2018-07-26","arxiv_id":"1807.10111","n_code_links":0,"syntology":null},{"paper":null,"slug":"recurrent-fusion-network-for-image-captioning","title":"Recurrent Fusion Network for Image Captioning","date":"2018-07-26","arxiv_id":"1807.09986","n_code_links":0,"syntology":null},{"paper":"/paper/unified-perceptual-parsing-for-scene","slug":"unified-perceptual-parsing-for-scene","title":"Unified Perceptual Parsing for Scene Understanding","date":"2018-07-26","arxiv_id":"1807.10221","n_code_links":25,"syntology":{"ran":25,"of":28,"n_ran_checked":18,"n_instrument":7,"unverified":3,"pointer_only":10,"phrase":"25 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 1 honoured, 0 violated, 17 with no contract checked; 7 where Syntology's instrument failed) · 3 unverified","official":{"repos":["CSAILVision/unifiedparsing"],"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":"coreset-based-neural-network-compression","title":"Coreset-Based Neural Network Compression","date":"2018-07-25","arxiv_id":"1807.09810","n_code_links":0,"syntology":null},{"paper":null,"slug":"crossbar-aware-neural-network-pruning","title":"Crossbar-aware neural network pruning","date":"2018-07-25","arxiv_id":"1807.10816","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-resolution-networks-for-semantic","title":"Multi-Resolution Networks for Semantic Segmentation in Whole Slide Images","date":"2018-07-25","arxiv_id":"1807.09607","n_code_links":0,"syntology":null},{"paper":"/paper/two-at-once-enhancing-learning-and","slug":"two-at-once-enhancing-learning-and","title":"Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net","date":"2018-07-25","arxiv_id":"1807.09441","n_code_links":25,"syntology":{"ran":8,"of":16,"n_ran_checked":7,"n_instrument":1,"unverified":8,"pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 8 unverified","official":{"repos":["XingangPan/IBN-Net"],"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":"dermoscopic-image-analysis-for-isic-challenge","title":"Dermoscopic Image Analysis for ISIC Challenge 2018","date":"2018-07-24","arxiv_id":"1807.08948","n_code_links":0,"syntology":null},{"paper":"/paper/learning-discriminative-video-representations","slug":"learning-discriminative-video-representations","title":"Contrastive Video Representation Learning via Adversarial Perturbations","date":"2018-07-24","arxiv_id":"1807.09380","n_code_links":0,"syntology":null},{"paper":null,"slug":"text-classification-based-on-multiple-block","title":"Text Classification based on Multiple Block Convolutional Highways","date":"2018-07-23","arxiv_id":"1807.09602","n_code_links":0,"syntology":null},{"paper":"/paper/macro-micro-adversarial-network-for-human","slug":"macro-micro-adversarial-network-for-human","title":"Macro-Micro Adversarial Network for Human Parsing","date":"2018-07-22","arxiv_id":"1807.08260","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-post-processing-method-to-improve-the-white","title":"A post-processing method to improve the white matter hyperintensity segmentation accuracy for randomly-initialized U-net","date":"2018-07-21","arxiv_id":"1807.10600","n_code_links":0,"syntology":null},{"paper":null,"slug":"competition-vs-concatenation-in-skip","title":"Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks","date":"2018-07-20","arxiv_id":"1807.07803","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimize-deep-convolutional-neural-network","title":"Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy","date":"2018-07-20","arxiv_id":"1807.07948","n_code_links":0,"syntology":null},{"paper":"/paper/clarinet-parallel-wave-generation-in-end-to","slug":"clarinet-parallel-wave-generation-in-end-to","title":"ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech","date":"2018-07-19","arxiv_id":"1807.07281","n_code_links":5,"syntology":{"ran":10,"of":13,"n_ran_checked":10,"n_instrument":0,"unverified":3,"pointer_only":1,"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) · 3 unverified","official":null}},{"paper":null,"slug":"improving-simple-models-with-confidence","title":"Improving Simple Models with Confidence Profiles","date":"2018-07-19","arxiv_id":"1807.07506","n_code_links":0,"syntology":null},{"paper":null,"slug":"in-pixels-we-trust-from-pixel-labeling-to","title":"In pixels we trust: From Pixel Labeling to Object Localization and Scene Categorization","date":"2018-07-19","arxiv_id":"1807.07284","n_code_links":0,"syntology":null},{"paper":null,"slug":"isic-2018-a-method-for-lesion-segmentation","title":"ISIC 2018-A Method for Lesion Segmentation","date":"2018-07-19","arxiv_id":"1807.07391","n_code_links":0,"syntology":null},{"paper":null,"slug":"transfer-learning-for-action-unit-recognition","title":"Transfer Learning for Action Unit Recognition","date":"2018-07-19","arxiv_id":"1807.07556","n_code_links":0,"syntology":null},{"paper":null,"slug":"3d-global-convolutional-adversarial-network","title":"3D Global Convolutional Adversarial Network\\\\ for Prostate MR Volume Segmentation","date":"2018-07-18","arxiv_id":"1807.06742","n_code_links":0,"syntology":null},{"paper":"/paper/unet-a-nested-u-net-architecture-for-medical","slug":"unet-a-nested-u-net-architecture-for-medical","title":"UNet++: A Nested U-Net Architecture for Medical Image Segmentation","date":"2018-07-18","arxiv_id":"1807.10165","n_code_links":34,"syntology":{"ran":21,"of":28,"n_ran_checked":18,"n_instrument":3,"unverified":7,"pointer_only":3,"phrase":"21 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 1 honoured, 1 violated, 16 with no contract checked; 3 where Syntology's instrument failed) · 7 unverified","official":{"repos":["MrGiovanni/Nested-UNet"],"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":"a-dense-cnn-approach-for-skin-lesion","title":"A Dense CNN approach for skin lesion classification","date":"2018-07-17","arxiv_id":"1807.06416","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":31,"syntology":{"ran":13,"of":22,"n_ran_checked":10,"n_instrument":3,"unverified":9,"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","official":null}},{"paper":"/paper/pointseg-real-time-semantic-segmentation","slug":"pointseg-real-time-semantic-segmentation","title":"PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud","date":"2018-07-17","arxiv_id":"1807.06288","n_code_links":3,"syntology":null},{"paper":"/paper/a-dataset-of-laryngeal-endoscopic-images-with","slug":"a-dataset-of-laryngeal-endoscopic-images-with","title":"A Dataset of Laryngeal Endoscopic Images with Comparative Study on Convolution Neural Network Based Semantic Segmentation","date":"2018-07-16","arxiv_id":"1807.06081","n_code_links":1,"syntology":null},{"paper":"/paper/bipedal-walking-robot-using-deep","slug":"bipedal-walking-robot-using-deep","title":"Bipedal Walking Robot using Deep Deterministic Policy Gradient","date":"2018-07-16","arxiv_id":"1807.05924","n_code_links":3,"syntology":null},{"paper":null,"slug":"brief-backward-reduction-of-cnns-with","title":"BRIEF: Backward Reduction of CNNs with Information Flow Analysis","date":"2018-07-16","arxiv_id":"1807.05726","n_code_links":0,"syntology":null}],"record_sha256":"ee50e250761e6e6fbde21bde5327efed65de7187b090f4152e061139d332b13e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}