{"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/96","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":96,"pages_in_order":104,"rows_per_page":100,"rows":[9501,9600],"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/95","next":"/method/relu/papers/97","papers":[{"paper":"/paper/deep-photo-enhancer-unpaired-learning-for","slug":"deep-photo-enhancer-unpaired-learning-for","title":"Deep Photo Enhancer: Unpaired Learning for Image Enhancement From Photographs With GANs","date":"2018-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/denseaspp-for-semantic-segmentation-in-street","slug":"denseaspp-for-semantic-segmentation-in-street","title":"DenseASPP for Semantic Segmentation in Street Scenes","date":"2018-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/docunet-document-image-unwarping-via-a","slug":"docunet-document-image-unwarping-via-a","title":"DocUNet: Document Image Unwarping via a Stacked U-Net","date":"2018-06-01","arxiv_id":null,"n_code_links":1,"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":null,"slug":"interleaved-structured-sparse-convolutional","title":"Interleaved Structured Sparse Convolutional Neural Networks","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/neural-proximal-gradient-descent-for","slug":"neural-proximal-gradient-descent-for","title":"Neural Proximal Gradient Descent for Compressive Imaging","date":"2018-06-01","arxiv_id":"1806.03963","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":"/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":"/paper/unsupervised-feature-learning-via-non-1","slug":"unsupervised-feature-learning-via-non-1","title":"Unsupervised Feature Learning via Non-Parametric Instance Discrimination","date":"2018-06-01","arxiv_id":null,"n_code_links":4,"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":"fully-automated-organ-segmentation-in-male","title":"Fully Automated Organ Segmentation in Male Pelvic CT Images","date":"2018-05-31","arxiv_id":"1805.12526","n_code_links":0,"syntology":null},{"paper":"/paper/scaling-provable-adversarial-defenses","slug":"scaling-provable-adversarial-defenses","title":"Scaling provable adversarial defenses","date":"2018-05-31","arxiv_id":"1805.12514","n_code_links":4,"syntology":null},{"paper":"/paper/deep-reinforcement-learning-in-a-handful-of","slug":"deep-reinforcement-learning-in-a-handful-of","title":"Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models","date":"2018-05-30","arxiv_id":"1805.12114","n_code_links":9,"syntology":{"ran":12,"of":19,"n_ran_checked":7,"n_instrument":5,"unverified":7,"pointer_only":17,"phrase":"12 ran (of which 5 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 7 unverified","official":{"repos":["kchua/handful-of-trials"],"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":"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":null,"slug":"runresidual-u-net-for-computer-aided","title":"RUN:Residual U-Net for Computer-Aided Detection of Pulmonary Nodules without Candidate Selection","date":"2018-05-30","arxiv_id":"1805.11856","n_code_links":0,"syntology":null},{"paper":"/paper/towards-understanding-the-role-of-over","slug":"towards-understanding-the-role-of-over","title":"Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks","date":"2018-05-30","arxiv_id":"1805.12076","n_code_links":2,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["bneyshabur/over-parametrization"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"a-novel-channel-pruning-method-for-deep","title":"A novel channel pruning method for deep neural network compression","date":"2018-05-29","arxiv_id":"1805.11394","n_code_links":0,"syntology":null},{"paper":"/paper/microscopy-cell-segmentation-via","slug":"microscopy-cell-segmentation-via","title":"Microscopy Cell Segmentation via Convolutional LSTM Networks","date":"2018-05-29","arxiv_id":"1805.11247","n_code_links":3,"syntology":null},{"paper":null,"slug":"representational-power-of-relu-networks-and","title":"Representational Power of ReLU Networks and Polynomial Kernels: Beyond Worst-Case Analysis","date":"2018-05-29","arxiv_id":"1805.11405","n_code_links":0,"syntology":null},{"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/sacrificing-accuracy-for-reduced-computation","slug":"sacrificing-accuracy-for-reduced-computation","title":"Dynamically Sacrificing Accuracy for Reduced Computation: Cascaded Inference Based on Softmax Confidence","date":"2018-05-28","arxiv_id":"1805.10982","n_code_links":1,"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":"pathology-segmentation-using-distributional","title":"Pathology Segmentation using Distributional Differences to Images of Healthy Origin","date":"2018-05-25","arxiv_id":"1805.10344","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":null,"slug":"residual-networks-as-geodesic-flows-of","title":"Residual Networks as Geodesic Flows of Diffeomorphisms","date":"2018-05-24","arxiv_id":"1805.09585","n_code_links":0,"syntology":null},{"paper":null,"slug":"building-extraction-at-scale-using","title":"Building Extraction at Scale using Convolutional Neural Network: Mapping of the United States","date":"2018-05-23","arxiv_id":"1805.08946","n_code_links":0,"syntology":null},{"paper":"/paper/sniper-efficient-multi-scale-training","slug":"sniper-efficient-multi-scale-training","title":"SNIPER: Efficient Multi-Scale Training","date":"2018-05-23","arxiv_id":"1805.09300","n_code_links":4,"syntology":null},{"paper":null,"slug":"a-tropical-approach-to-neural-networks-with","title":"A Tropical Approach to Neural Networks with Piecewise Linear Activations","date":"2018-05-22","arxiv_id":"1805.08749","n_code_links":0,"syntology":null},{"paper":null,"slug":"aria-utilizing-richards-curve-for-controlling","title":"ARiA: Utilizing Richard's Curve for Controlling the Non-monotonicity of the Activation Function in Deep Neural Nets","date":"2018-05-22","arxiv_id":"1805.08878","n_code_links":0,"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},{"paper":null,"slug":"deep-denoising-rate-optimal-recovery-of","title":"Rate-Optimal Denoising with Deep Neural Networks","date":"2018-05-22","arxiv_id":"1805.08855","n_code_links":0,"syntology":null},{"paper":"/paper/expectation-propagation-a-probabilistic-view","slug":"expectation-propagation-a-probabilistic-view","title":"Mean Field Theory of Activation Functions in Deep Neural Networks","date":"2018-05-22","arxiv_id":"1805.08786","n_code_links":2,"syntology":null},{"paper":null,"slug":"training-convolutional-networks-with-web","title":"Training Convolutional Networks with Web Images","date":"2018-05-22","arxiv_id":"1805.08416","n_code_links":0,"syntology":null},{"paper":null,"slug":"compression-of-deep-convolutional-neural-1","title":"Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints","date":"2018-05-21","arxiv_id":"1805.08303","n_code_links":0,"syntology":null},{"paper":"/paper/small-steps-and-giant-leaps-minimal-newton","slug":"small-steps-and-giant-leaps-minimal-newton","title":"Small steps and giant leaps: Minimal Newton solvers for Deep Learning","date":"2018-05-21","arxiv_id":"1805.08095","n_code_links":6,"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":{"repos":["jotaf98/curveball"],"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":"cappronet-deep-feature-learning-via","title":"CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces","date":"2018-05-19","arxiv_id":"1805.07621","n_code_links":0,"syntology":null},{"paper":"/paper/sampling-free-variational-inference-of","slug":"sampling-free-variational-inference-of","title":"Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation","date":"2018-05-19","arxiv_id":"1805.07654","n_code_links":1,"syntology":null},{"paper":"/paper/a-theoretical-explanation-for-perplexing","slug":"a-theoretical-explanation-for-perplexing","title":"A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations","date":"2018-05-18","arxiv_id":"1805.07039","n_code_links":1,"syntology":null},{"paper":"/paper/multi-level-wavelet-cnn-for-image-restoration","slug":"multi-level-wavelet-cnn-for-image-restoration","title":"Multi-level Wavelet-CNN for Image Restoration","date":"2018-05-18","arxiv_id":"1805.07071","n_code_links":5,"syntology":null},{"paper":"/paper/norm-preservation-why-residual-networks-can","slug":"norm-preservation-why-residual-networks-can","title":"Norm-Preservation: Why Residual Networks Can Become Extremely Deep?","date":"2018-05-18","arxiv_id":"1805.07477","n_code_links":1,"syntology":null},{"paper":null,"slug":"reconstruction-of-training-samples-from-loss","title":"Reconstruction of training samples from loss functions","date":"2018-05-18","arxiv_id":"1805.07337","n_code_links":0,"syntology":null},{"paper":"/paper/tropical-geometry-of-deep-neural-networks","slug":"tropical-geometry-of-deep-neural-networks","title":"Tropical Geometry of Deep Neural Networks","date":"2018-05-18","arxiv_id":"1805.07091","n_code_links":1,"syntology":null},{"paper":null,"slug":"two-geometric-input-transformation-methods","title":"Two geometric input transformation methods for fast online reinforcement learning with neural nets","date":"2018-05-18","arxiv_id":"1805.07476","n_code_links":0,"syntology":null},{"paper":null,"slug":"identifying-object-states-in-cooking-related","title":"Identifying Object States in Cooking-Related Images","date":"2018-05-17","arxiv_id":"1805.06956","n_code_links":0,"syntology":null},{"paper":null,"slug":"interpolatron-interpolation-or-extrapolation","title":"Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks","date":"2018-05-17","arxiv_id":"1805.06753","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparison-of-modeling-units-in-sequence-to","title":"A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese","date":"2018-05-16","arxiv_id":"1805.06239","n_code_links":0,"syntology":null},{"paper":"/paper/deep-segmentation-and-registration-in-x-ray","slug":"deep-segmentation-and-registration-in-x-ray","title":"Deep Segmentation and Registration in X-Ray Angiography Video","date":"2018-05-16","arxiv_id":"1805.06406","n_code_links":1,"syntology":null},{"paper":"/paper/object-detection-at-200-frames-per-second","slug":"object-detection-at-200-frames-per-second","title":"Object detection at 200 Frames Per Second","date":"2018-05-16","arxiv_id":"1805.06361","n_code_links":1,"syntology":null},{"paper":"/paper/advances-in-experience-replay","slug":"advances-in-experience-replay","title":"Advances in Experience Replay","date":"2018-05-15","arxiv_id":"1805.05536","n_code_links":1,"syntology":null},{"paper":"/paper/do-deep-reinforcement-learning-agents-model","slug":"do-deep-reinforcement-learning-agents-model","title":"Do deep reinforcement learning agents model intentions?","date":"2018-05-15","arxiv_id":"1805.06020","n_code_links":1,"syntology":null},{"paper":"/paper/cycle-dehaze-enhanced-cyclegan-for-single","slug":"cycle-dehaze-enhanced-cyclegan-for-single","title":"Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing","date":"2018-05-14","arxiv_id":"1805.05308","n_code_links":3,"syntology":null},{"paper":"/paper/on-the-practical-computational-power-of","slug":"on-the-practical-computational-power-of","title":"On the Practical Computational Power of Finite Precision RNNs for Language Recognition","date":"2018-05-13","arxiv_id":"1805.04908","n_code_links":1,"syntology":null},{"paper":null,"slug":"classification-of-protein-crystallization-x","title":"Classification of Protein Crystallization X-Ray Images Using Major Convolutional Neural Network Architectures","date":"2018-05-11","arxiv_id":"1805.04563","n_code_links":0,"syntology":null},{"paper":null,"slug":"laconic-deep-learning-computing","title":"Laconic Deep Learning Computing","date":"2018-05-10","arxiv_id":"1805.04513","n_code_links":0,"syntology":null},{"paper":null,"slug":"monotone-learning-with-rectified-wire","title":"Monotone Learning with Rectified Wire Networks","date":"2018-05-10","arxiv_id":"1805.03963","n_code_links":0,"syntology":null},{"paper":null,"slug":"unifying-data-model-and-hybrid-parallelism-in","title":"Unifying Data, Model and Hybrid Parallelism in Deep Learning via Tensor Tiling","date":"2018-05-10","arxiv_id":"1805.04170","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-resnext-model-architecture-for","slug":"evaluating-resnext-model-architecture-for","title":"Evaluating ResNeXt Model Architecture for Image Classification","date":"2018-05-09","arxiv_id":"1805.08700","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-performance-evaluation-of-convolutional","title":"A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofing","date":"2018-05-08","arxiv_id":"1805.04176","n_code_links":0,"syntology":null},{"paper":null,"slug":"fully-automated-segmentation-of","title":"Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images","date":"2018-05-08","arxiv_id":"1805.03278","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-short-cut-connections-for-object","title":"Learning Short-Cut Connections for Object Counting","date":"2018-05-08","arxiv_id":"1805.02919","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-effectiveness-of-instance-normalization-a","title":"The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing","date":"2018-05-08","arxiv_id":"1805.03305","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantization-mimic-towards-very-tiny-cnn-for","title":"Quantization Mimic: Towards Very Tiny CNN for Object Detection","date":"2018-05-06","arxiv_id":"1805.02152","n_code_links":0,"syntology":null},{"paper":null,"slug":"squeezejet-high-level-synthesis-accelerator","title":"SqueezeJet: High-level Synthesis Accelerator Design for Deep Convolutional Neural Networks","date":"2018-05-06","arxiv_id":"1805.08695","n_code_links":0,"syntology":null},{"paper":null,"slug":"rifcn-recurrent-network-in-fully","title":"RiFCN: Recurrent Network in Fully Convolutional Network for Semantic Segmentation of High Resolution Remote Sensing Images","date":"2018-05-05","arxiv_id":"1805.02091","n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-feature-learning-via-non","slug":"unsupervised-feature-learning-via-non","title":"Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination","date":"2018-05-05","arxiv_id":"1805.01978","n_code_links":15,"syntology":{"ran":19,"of":23,"n_ran_checked":17,"n_instrument":2,"unverified":4,"pointer_only":16,"phrase":"19 ran (of which 14 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","official":{"repos":["zhirongw/lemniscate.pytorch"],"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":"framewise-approach-in-multimodal-emotion","title":"Framewise approach in multimodal emotion recognition in OMG challenge","date":"2018-05-03","arxiv_id":"1805.01369","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-data-driven-residential-transformer","title":"A Data-Driven Residential Transformer Overloading Risk Assessment Method","date":"2018-05-02","arxiv_id":"1805.00630","n_code_links":0,"syntology":null},{"paper":"/paper/accelerating-neural-transformer-via-an","slug":"accelerating-neural-transformer-via-an","title":"Accelerating Neural Transformer via an Average Attention Network","date":"2018-05-02","arxiv_id":"1805.00631","n_code_links":1,"syntology":{"ran":7,"of":19,"n_ran_checked":7,"n_instrument":0,"unverified":12,"pointer_only":0,"phrase":"7 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; 0 where Syntology's instrument failed) · 12 unverified","official":{"repos":["bzhangXMU/transformer-aan"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":12,"ran_from_kinds":["official"]}}},{"paper":"/paper/exploring-the-limits-of-weakly-supervised","slug":"exploring-the-limits-of-weakly-supervised","title":"Exploring the Limits of Weakly Supervised Pretraining","date":"2018-05-02","arxiv_id":"1805.00932","n_code_links":4,"syntology":null},{"paper":null,"slug":"localization-a-missing-link-in-the-pipeline","title":"Localization: A Missing Link in the Pipeline of Object Matching and Registration","date":"2018-05-01","arxiv_id":"1805.00223","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-anti-fraud-system-for-car-insurance-claim","title":"An Anti-fraud System for Car Insurance Claim Based on Visual Evidence","date":"2018-04-30","arxiv_id":"1804.11207","n_code_links":0,"syntology":null},{"paper":"/paper/machine-learning-for-exam-triage","slug":"machine-learning-for-exam-triage","title":"Machine Learning for Exam Triage","date":"2018-04-30","arxiv_id":"1805.00503","n_code_links":1,"syntology":null},{"paper":null,"slug":"stack-u-net-refinement-network-for-image","title":"Stack-U-Net: Refinement Network for Image Segmentation on the Example of Optic Disc and Cup","date":"2018-04-30","arxiv_id":"1804.11294","n_code_links":0,"syntology":null},{"paper":null,"slug":"ultra-power-efficient-cnn-domain-specific","title":"Ultra Power-Efficient CNN Domain Specific Accelerator with 9.3TOPS/Watt for Mobile and Embedded Applications","date":"2018-04-30","arxiv_id":"1805.00361","n_code_links":0,"syntology":null},{"paper":null,"slug":"craft-complementary-recommendations-using","title":"CRAFT: Complementary Recommendations Using Adversarial Feature Transformer","date":"2018-04-29","arxiv_id":"1804.10871","n_code_links":0,"syntology":null},{"paper":null,"slug":"treesegnet-adaptive-tree-cnns-for","title":"TreeSegNet: Adaptive Tree CNNs for Subdecimeter Aerial Image Segmentation","date":"2018-04-29","arxiv_id":"1804.10879","n_code_links":0,"syntology":null},{"paper":null,"slug":"cram-clued-recurrent-attention-model","title":"CRAM: Clued Recurrent Attention Model","date":"2018-04-28","arxiv_id":"1804.10844","n_code_links":0,"syntology":null},{"paper":null,"slug":"spiking-deep-residual-network","title":"Spiking Deep Residual Network","date":"2018-04-28","arxiv_id":"1805.01352","n_code_links":0,"syntology":null},{"paper":"/paper/syllable-based-sequence-to-sequence-speech","slug":"syllable-based-sequence-to-sequence-speech","title":"Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese","date":"2018-04-28","arxiv_id":"1804.10752","n_code_links":1,"syntology":null},{"paper":"/paper/accelerator-aware-pruning-for-convolutional","slug":"accelerator-aware-pruning-for-convolutional","title":"Accelerator-Aware Pruning for Convolutional Neural Networks","date":"2018-04-26","arxiv_id":"1804.09862","n_code_links":3,"syntology":null},{"paper":null,"slug":"iamnn-iterative-and-adaptive-mobile-neural","title":"IamNN: Iterative and Adaptive Mobile Neural Network for Efficient Image Classification","date":"2018-04-26","arxiv_id":"1804.10123","n_code_links":0,"syntology":null},{"paper":"/paper/the-best-of-both-worlds-combining-recent","slug":"the-best-of-both-worlds-combining-recent","title":"The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation","date":"2018-04-26","arxiv_id":"1804.09849","n_code_links":3,"syntology":null},{"paper":"/paper/learning-a-discriminative-feature-network-for","slug":"learning-a-discriminative-feature-network-for","title":"Learning a Discriminative Feature Network for Semantic Segmentation","date":"2018-04-25","arxiv_id":"1804.09337","n_code_links":3,"syntology":{"ran":3,"of":8,"n_ran_checked":3,"n_instrument":0,"unverified":5,"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) · 5 unverified","official":null}},{"paper":null,"slug":"multiagent-soft-q-learning","title":"Multiagent Soft Q-Learning","date":"2018-04-25","arxiv_id":"1804.09817","n_code_links":0,"syntology":null},{"paper":"/paper/towards-fast-computation-of-certified","slug":"towards-fast-computation-of-certified","title":"Towards Fast Computation of Certified Robustness for ReLU Networks","date":"2018-04-25","arxiv_id":"1804.09699","n_code_links":6,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":2,"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":["huanzhang12/CertifiedReLURobustness"],"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/densefuse-a-fusion-approach-to-infrared-and","slug":"densefuse-a-fusion-approach-to-infrared-and","title":"DenseFuse: A Fusion Approach to Infrared and Visible Images","date":"2018-04-23","arxiv_id":"1804.08361","n_code_links":4,"syntology":null},{"paper":null,"slug":"study-of-residual-networks-for-image","title":"Study of Residual Networks for Image Recognition","date":"2018-04-21","arxiv_id":"1805.00325","n_code_links":0,"syntology":null},{"paper":null,"slug":"generating-a-fusion-image-ones-identity-and","title":"Generating a Fusion Image: One's Identity and Another's Shape","date":"2018-04-20","arxiv_id":"1804.07455","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-individual-neuron-importance","title":"Understanding Neural Networks and Individual Neuron Importance via Information-Ordered Cumulative Ablation","date":"2018-04-18","arxiv_id":"1804.06679","n_code_links":0,"syntology":null},{"paper":"/paper/detnet-a-backbone-network-for-object","slug":"detnet-a-backbone-network-for-object","title":"DetNet: A Backbone network for Object Detection","date":"2018-04-17","arxiv_id":"1804.06215","n_code_links":2,"syntology":null},{"paper":"/paper/igcv2-interleaved-structured-sparse","slug":"igcv2-interleaved-structured-sparse","title":"IGCV$2$: Interleaved Structured Sparse Convolutional Neural Networks","date":"2018-04-17","arxiv_id":"1804.06202","n_code_links":2,"syntology":null},{"paper":"/paper/simple-baselines-for-human-pose-estimation","slug":"simple-baselines-for-human-pose-estimation","title":"Simple Baselines for Human Pose Estimation and Tracking","date":"2018-04-17","arxiv_id":"1804.06208","n_code_links":27,"syntology":null},{"paper":null,"slug":"segmentation-of-both-diseased-and-healthy","title":"Segmentation of both Diseased and Healthy Skin from Clinical Photographs in a Primary Care Setting","date":"2018-04-16","arxiv_id":"1804.05944","n_code_links":0,"syntology":null},{"paper":null,"slug":"sparsenet-a-sparse-densenet-for-image","title":"SparseNet: A Sparse DenseNet for Image Classification","date":"2018-04-15","arxiv_id":"1804.05340","n_code_links":0,"syntology":null},{"paper":null,"slug":"select-attend-and-transfer-light-learnable","title":"Select, Attend, and Transfer: Light, Learnable Skip Connections","date":"2018-04-14","arxiv_id":"1804.05181","n_code_links":0,"syntology":null},{"paper":"/paper/-cudnn-accelerating-deep-learning-frameworks","slug":"-cudnn-accelerating-deep-learning-frameworks","title":"μ-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batching","date":"2018-04-13","arxiv_id":"1804.04806","n_code_links":1,"syntology":null}],"record_sha256":"7c605a3d2c696fb707968ae205b6ac66586027476a6f9bd5b7562eb6900bb2fd","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}