{"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/dropout/papers/269","list_of":"/method/dropout","method":"Dropout","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":269,"pages_in_order":275,"rows_per_page":100,"rows":[26801,26900],"of":27472,"counts":{"archive_papers_tagged":27472,"with_a_code_link":12129,"where_syntology_ran_a_sample":3620,"not_listed_spam_title":0,"listed":27472,"listed_where_code_ran":3620,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3044,"every_run_a_failure_of_syntologys_instrument":576,"listed_with_a_run_with_no_instrument_failure":3044,"listed_every_run_a_failure_of_syntologys_instrument":576,"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/dropout","prev":"/method/dropout/papers/268","next":"/method/dropout/papers/270","papers":[{"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/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":"/paper/rs-net-regression-segmentation-3d-cnn-for","slug":"rs-net-regression-segmentation-3d-cnn-for","title":"RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours","date":"2018-07-28","arxiv_id":"1807.10972","n_code_links":1,"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":"/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":"/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":"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":"deep-contextual-multi-armed-bandits","title":"Deep Contextual Multi-armed Bandits","date":"2018-07-25","arxiv_id":"1807.09809","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":"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":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":"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":"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":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},{"paper":null,"slug":"deep-neural-network-ensemble-by-data","title":"Deep neural network ensemble by data augmentation and bagging for skin lesion classification","date":"2018-07-15","arxiv_id":"1807.05496","n_code_links":0,"syntology":null},{"paper":null,"slug":"near-real-time-hippocampus-segmentation-using","title":"Near Real-time Hippocampus Segmentation Using Patch-based Canonical Neural Network","date":"2018-07-15","arxiv_id":"1807.05482","n_code_links":0,"syntology":null},{"paper":null,"slug":"cascadecnn-pushing-the-performance-limits-of-1","title":"CascadeCNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks","date":"2018-07-13","arxiv_id":"1807.05053","n_code_links":0,"syntology":null},{"paper":null,"slug":"destnet-densely-fused-spatial-transformer","title":"DeSTNet: Densely Fused Spatial Transformer Networks","date":"2018-07-11","arxiv_id":"1807.04050","n_code_links":0,"syntology":null},{"paper":"/paper/universal-transformers","slug":"universal-transformers","title":"Universal Transformers","date":"2018-07-10","arxiv_id":"1807.03819","n_code_links":8,"syntology":{"ran":17,"of":25,"n_ran_checked":17,"n_instrument":0,"unverified":8,"pointer_only":24,"phrase":"17 ran (of which 8 constructed an object rather than computing a result; 17 with no instrument failure: 1 honoured, 0 violated, 16 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","official":{"repos":["tensorflow/tensor2tensor"],"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/when-work-matters-transforming-classical","slug":"when-work-matters-transforming-classical","title":"When Work Matters: Transforming Classical Network Structures to Graph CNN","date":"2018-07-07","arxiv_id":"1807.02653","n_code_links":0,"syntology":null},{"paper":null,"slug":"reversed-active-learning-based-atrous","title":"Reversed Active Learning based Atrous DenseNet for Pathological Image Classification","date":"2018-07-06","arxiv_id":"1807.02420","n_code_links":0,"syntology":null},{"paper":null,"slug":"variational-bayesian-dropout-pitfalls-and","title":"Variational Bayesian dropout: pitfalls and fixes","date":"2018-07-05","arxiv_id":"1807.01969","n_code_links":0,"syntology":null},{"paper":"/paper/bayesgrad-explaining-predictions-of-graph","slug":"bayesgrad-explaining-predictions-of-graph","title":"BayesGrad: Explaining Predictions of Graph Convolutional Networks","date":"2018-07-04","arxiv_id":"1807.01985","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["pfnet-research/bayesgrad"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/benchmarking-neural-network-robustness-to","slug":"benchmarking-neural-network-robustness-to","title":"Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations","date":"2018-07-04","arxiv_id":"1807.01697","n_code_links":2,"syntology":{"ran":6,"of":6,"n_ran_checked":4,"n_instrument":2,"unverified":0,"pointer_only":3,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/a-weakly-supervised-adaptive-densenet-for","slug":"a-weakly-supervised-adaptive-densenet-for","title":"A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities","date":"2018-07-03","arxiv_id":"1807.01257","n_code_links":1,"syntology":null},{"paper":null,"slug":"model-based-hand-pose-estimation-for","title":"Model-based Hand Pose Estimation for Generalized Hand Shape with Appearance Normalization","date":"2018-07-02","arxiv_id":"1807.00898","n_code_links":0,"syntology":null},{"paper":"/paper/conceptual-captions-a-cleaned-hypernymed","slug":"conceptual-captions-a-cleaned-hypernymed","title":"Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning","date":"2018-07-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/deep-k-means-re-training-and-parameter","slug":"deep-k-means-re-training-and-parameter","title":"Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions","date":"2018-07-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"dropout-training-data-dependent","title":"Dropout Training, Data-dependent Regularization, and Generalization Bounds","date":"2018-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/how-much-attention-do-you-need-a-granular","slug":"how-much-attention-do-you-need-a-granular","title":"How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures","date":"2018-07-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"language-informed-modeling-of-code-switched","title":"Language Informed Modeling of Code-Switched Text","date":"2018-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-longer-term-dependencies-in-rnns-1","title":"Learning Longer-term Dependencies in RNNs with Auxiliary Losses","date":"2018-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/multi-turn-response-selection-for-chatbots","slug":"multi-turn-response-selection-for-chatbots","title":"Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network","date":"2018-07-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/neural-machine-translation-techniques-for","slug":"neural-machine-translation-techniques-for","title":"Neural Machine Translation Techniques for Named Entity Transliteration","date":"2018-07-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"nict-self-training-approach-to-neural-machine","title":"NICT Self-Training Approach to Neural Machine Translation at NMT-2018","date":"2018-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/the-annotated-transformer","slug":"the-annotated-transformer","title":"The Annotated Transformer","date":"2018-07-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"automatic-rank-selection-for-high-speed","title":"Automatic Rank Selection for High-Speed Convolutional Neural Network","date":"2018-06-28","arxiv_id":"1806.10821","n_code_links":0,"syntology":null},{"paper":null,"slug":"dropout-based-active-learning-for-regression","title":"Dropout-based Active Learning for Regression","date":"2018-06-26","arxiv_id":"1806.09856","n_code_links":0,"syntology":null},{"paper":null,"slug":"gradient-acceleration-in-activation-functions","title":"Understanding Dropout as an Optimization Trick","date":"2018-06-26","arxiv_id":"1806.09783","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-implicit-bias-of-dropout","title":"On the Implicit Bias of Dropout","date":"2018-06-26","arxiv_id":"1806.09777","n_code_links":0,"syntology":null},{"paper":"/paper/deep-k-means-re-training-and-parameter-1","slug":"deep-k-means-re-training-and-parameter-1","title":"Deep $k$-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions","date":"2018-06-24","arxiv_id":"1806.09228","n_code_links":1,"syntology":null},{"paper":"/paper/probabilistic-natural-language-generation","slug":"probabilistic-natural-language-generation","title":"Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation","date":"2018-06-22","arxiv_id":"1806.08462","n_code_links":1,"syntology":null},{"paper":null,"slug":"faster-sgd-training-by-minibatch-persistency","title":"Faster SGD training by minibatch persistency","date":"2018-06-19","arxiv_id":"1806.07353","n_code_links":0,"syntology":null},{"paper":"/paper/recurrent-dnns-and-its-ensembles-on-the-timit","slug":"recurrent-dnns-and-its-ensembles-on-the-timit","title":"Recurrent DNNs and its Ensembles on the TIMIT Phone Recognition Task","date":"2018-06-19","arxiv_id":"1806.07186","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-comparison-of-transformer-and-recurrent","title":"A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation","date":"2018-06-18","arxiv_id":"1806.06957","n_code_links":0,"syntology":null},{"paper":"/paper/towards-gene-expression-convolutions-using","slug":"towards-gene-expression-convolutions-using","title":"Towards Gene Expression Convolutions using Gene Interaction Graphs","date":"2018-06-18","arxiv_id":"1806.06975","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-lip-reading-a-comparison-of-models-and","title":"Deep Lip Reading: a comparison of models and an online application","date":"2018-06-15","arxiv_id":"1806.06053","n_code_links":0,"syntology":null},{"paper":null,"slug":"age-and-gender-classification-from-ear-images","title":"Age and Gender Classification From Ear Images","date":"2018-06-14","arxiv_id":"1806.05742","n_code_links":0,"syntology":null},{"paper":null,"slug":"fire-ssd-wide-fire-modules-based-single-shot","title":"Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device","date":"2018-06-14","arxiv_id":"1806.05363","n_code_links":0,"syntology":null},{"paper":"/paper/an-evaluation-of-neural-machine-translation","slug":"an-evaluation-of-neural-machine-translation","title":"An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization","date":"2018-06-13","arxiv_id":"1806.05210","n_code_links":1,"syntology":null},{"paper":null,"slug":"automated-performance-assessment-in","title":"Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks","date":"2018-06-13","arxiv_id":"1806.05154","n_code_links":0,"syntology":null},{"paper":null,"slug":"multilingual-end-to-end-speech-recognition","title":"Multilingual End-to-End Speech Recognition with A Single Transformer on Low-Resource Languages","date":"2018-06-12","arxiv_id":"1806.05059","n_code_links":0,"syntology":null},{"paper":null,"slug":"sample-dropout-for-audio-scene-classification","title":"Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network","date":"2018-06-12","arxiv_id":"1806.04422","n_code_links":0,"syntology":null},{"paper":"/paper/dropback-continuous-pruning-during-training","slug":"dropback-continuous-pruning-during-training","title":"Full deep neural network training on a pruned weight budget","date":"2018-06-11","arxiv_id":"1806.06949","n_code_links":1,"syntology":null},{"paper":"/paper/improving-language-understanding-by","slug":"improving-language-understanding-by","title":"Improving Language Understanding by Generative Pre-Training","date":"2018-06-11","arxiv_id":null,"n_code_links":13,"syntology":null},{"paper":null,"slug":"when-and-where-do-feed-forward-neural","title":"When and where do feed-forward neural networks learn localist representations?","date":"2018-06-11","arxiv_id":"1806.03934","n_code_links":0,"syntology":null},{"paper":"/paper/dank-learning-generating-memes-using-deep","slug":"dank-learning-generating-memes-using-deep","title":"Dank Learning: Generating Memes Using Deep Neural Networks","date":"2018-06-08","arxiv_id":"1806.04510","n_code_links":3,"syntology":null},{"paper":null,"slug":"revisiting-the-importance-of-individual-units","title":"Revisiting the Importance of Individual Units in CNNs via Ablation","date":"2018-06-07","arxiv_id":"1806.02891","n_code_links":0,"syntology":null},{"paper":"/paper/deep-neural-networks-with-multi-branch","slug":"deep-neural-networks-with-multi-branch","title":"Deep Neural Networks with Multi-Branch Architectures Are Less Non-Convex","date":"2018-06-06","arxiv_id":"1806.01845","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":1,"n_instrument":3,"unverified":0,"pointer_only":4,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["hongyanz/multibranch"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"state-classification-with-cnn","title":"State Classification with CNN","date":"2018-06-05","arxiv_id":"1806.03973","n_code_links":0,"syntology":null},{"paper":"/paper/backdrop-stochastic-backpropagation","slug":"backdrop-stochastic-backpropagation","title":"Backdrop: Stochastic Backpropagation","date":"2018-06-04","arxiv_id":"1806.01337","n_code_links":1,"syntology":null},{"paper":null,"slug":"meta-learner-with-linear-nulling","title":"Meta-Learner with Linear Nulling","date":"2018-06-04","arxiv_id":"1806.01010","n_code_links":0,"syntology":null},{"paper":null,"slug":"voice-imitating-text-to-speech-neural","title":"Voice Imitating Text-to-Speech Neural Networks","date":"2018-06-04","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/dense-information-flow-for-neural-machine","slug":"dense-information-flow-for-neural-machine","title":"Dense Information Flow for Neural Machine Translation","date":"2018-06-03","arxiv_id":"1806.00722","n_code_links":1,"syntology":null},{"paper":null,"slug":"study-and-development-of-a-computer-aided","title":"Study and development of a Computer-Aided Diagnosis system for classification of chest x-ray images using convolutional neural networks pre-trained for ImageNet and data augmentation","date":"2018-06-03","arxiv_id":"1806.00839","n_code_links":0,"syntology":null},{"paper":null,"slug":"sufficient-conditions-for-idealised-models-to","title":"Sufficient Conditions for Idealised Models to Have No Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks","date":"2018-06-02","arxiv_id":"1806.00667","n_code_links":0,"syntology":null},{"paper":"/paper/a-pid-controller-approach-for-stochastic","slug":"a-pid-controller-approach-for-stochastic","title":"A PID Controller Approach for Stochastic Optimization of Deep Networks","date":"2018-06-01","arxiv_id":null,"n_code_links":3,"syntology":null},{"paper":"/paper/cartoongan-generative-adversarial-networks","slug":"cartoongan-generative-adversarial-networks","title":"CartoonGAN: Generative Adversarial Networks for Photo Cartoonization","date":"2018-06-01","arxiv_id":null,"n_code_links":6,"syntology":null},{"paper":null,"slug":"categorizing-concepts-with-basic-level-for","title":"Categorizing Concepts With Basic Level for Vision-to-Language","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"clip-q-deep-network-compression-learning-by","title":"CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/deep-diffeomorphic-transformer-networks","slug":"deep-diffeomorphic-transformer-networks","title":"Deep Diffeomorphic Transformer Networks","date":"2018-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"dmcb-at-semeval-2018-task-1-transfer-learning","title":"DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"hydranets-specialized-dynamic-architectures","title":"HydraNets: Specialized Dynamic Architectures for Efficient Inference","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/radio-galaxy-morphology-generation-using-dnn","slug":"radio-galaxy-morphology-generation-using-dnn","title":"Radio Galaxy Morphology Generation Using DNN Autoencoder and Gaussian Mixture Models","date":"2018-06-01","arxiv_id":"1806.00398","n_code_links":1,"syntology":null},{"paper":null,"slug":"single-image-dehazing-via-conditional","title":"Single Image Dehazing via Conditional Generative Adversarial Network","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"the-power-of-ensembles-for-active-learning-in","title":"The Power of Ensembles for Active Learning in Image Classification","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/two-step-quantization-for-low-bit-neural","slug":"two-step-quantization-for-low-bit-neural","title":"Two-Step Quantization for Low-Bit Neural Networks","date":"2018-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"weakly-supervised-phrase-localization-with","title":"Weakly Supervised Phrase Localization With Multi-Scale Anchored Transformer Network","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"mpdcompress-matrix-permutation-decomposition","title":"MPDCompress - Matrix Permutation Decomposition Algorithm for Deep Neural Network Compression","date":"2018-05-30","arxiv_id":"1805.12085","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-function-convolutional-neural-networks","title":"Multi-function Convolutional Neural Networks for Improving Image Classification Performance","date":"2018-05-30","arxiv_id":"1805.11788","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-under-privileged-information","slug":"deep-learning-under-privileged-information","title":"Deep Learning under Privileged Information Using Heteroscedastic Dropout","date":"2018-05-29","arxiv_id":"1805.11614","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":5,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["johnwlambert/dlupi-heteroscedastic-dropout"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/adaptive-network-sparsification-with","slug":"adaptive-network-sparsification-with","title":"Adaptive Network Sparsification with Dependent Variational Beta-Bernoulli Dropout","date":"2018-05-28","arxiv_id":"1805.10896","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":null}},{"paper":"/paper/genattack-practical-black-box-attacks-with","slug":"genattack-practical-black-box-attacks-with","title":"GenAttack: Practical Black-box Attacks with Gradient-Free Optimization","date":"2018-05-28","arxiv_id":"1805.11090","n_code_links":3,"syntology":{"ran":5,"of":12,"n_ran_checked":5,"n_instrument":0,"unverified":7,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","official":{"repos":["nesl/adversarial_genattack"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/improving-the-resolution-of-cnn-feature-maps","slug":"improving-the-resolution-of-cnn-feature-maps","title":"Improving the Resolution of CNN Feature Maps Efficiently with Multisampling","date":"2018-05-28","arxiv_id":"1805.10766","n_code_links":2,"syntology":null},{"paper":"/paper/theory-and-experiments-on-vector-quantized","slug":"theory-and-experiments-on-vector-quantized","title":"Theory and Experiments on Vector Quantized Autoencoders","date":"2018-05-28","arxiv_id":"1805.11063","n_code_links":2,"syntology":null},{"paper":null,"slug":"compact-and-computationally-efficient","title":"Compact and Computationally Efficient Representation of Deep Neural Networks","date":"2018-05-27","arxiv_id":"1805.10692","n_code_links":0,"syntology":null},{"paper":null,"slug":"heterogeneous-bitwidth-binarization-in","title":"Heterogeneous Bitwidth Binarization in Convolutional Neural Networks","date":"2018-05-25","arxiv_id":"1805.10368","n_code_links":0,"syntology":null},{"paper":null,"slug":"three-dimensional-radiotherapy-dose","title":"Three-Dimensional Radiotherapy Dose Prediction on Head and Neck Cancer Patients with a Hierarchically Densely Connected U-net Deep Learning Architecture","date":"2018-05-25","arxiv_id":"1805.10397","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-scale-densenet-based-electricity-theft","title":"Multi-Scale DenseNet-Based Electricity Theft Detection","date":"2018-05-24","arxiv_id":"1805.09591","n_code_links":0,"syntology":null},{"paper":"/paper/3d-human-pose-estimation-with-relational","slug":"3d-human-pose-estimation-with-relational","title":"3D Human Pose Estimation with Relational Networks","date":"2018-05-23","arxiv_id":"1805.08961","n_code_links":1,"syntology":null},{"paper":null,"slug":"approximate-random-dropout","title":"Approximate Random Dropout","date":"2018-05-23","arxiv_id":"1805.08939","n_code_links":0,"syntology":null},{"paper":"/paper/excitation-dropout-encouraging-plasticity-in","slug":"excitation-dropout-encouraging-plasticity-in","title":"Excitation Dropout: Encouraging Plasticity in Deep Neural Networks","date":"2018-05-23","arxiv_id":"1805.09092","n_code_links":1,"syntology":null},{"paper":"/paper/pushing-the-bounds-of-dropout","slug":"pushing-the-bounds-of-dropout","title":"Pushing the bounds of dropout","date":"2018-05-23","arxiv_id":"1805.09208","n_code_links":1,"syntology":null},{"paper":null,"slug":"cascadecnn-pushing-the-performance-limits-of","title":"CascadeCNN: Pushing the performance limits of quantisation","date":"2018-05-22","arxiv_id":"1805.08743","n_code_links":0,"syntology":null}],"record_sha256":"8bd7d363282b50f354a205941b0fb02866245f672a8740cb373c2847e3ae16be","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}