{"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/dense-connections/papers/291","list_of":"/method/dense-connections","method":"Dense Connections","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":291,"pages_in_order":293,"rows_per_page":100,"rows":[29001,29100],"of":29230,"counts":{"archive_papers_tagged":29230,"with_a_code_link":12972,"where_syntology_ran_a_sample":3929,"not_listed_spam_title":0,"listed":29230,"listed_where_code_ran":3929,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3303,"every_run_a_failure_of_syntologys_instrument":626,"listed_with_a_run_with_no_instrument_failure":3303,"listed_every_run_a_failure_of_syntologys_instrument":626,"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/dense-connections","prev":"/method/dense-connections/papers/290","next":"/method/dense-connections/papers/292","papers":[{"paper":null,"slug":"deep-mixture-of-diverse-experts-for-large","title":"Deep Mixture of Diverse Experts for Large-Scale Visual Recognition","date":"2017-06-24","arxiv_id":"1706.07901","n_code_links":0,"syntology":null},{"paper":null,"slug":"balanced-quantization-an-effective-and","title":"Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks","date":"2017-06-22","arxiv_id":"1706.07145","n_code_links":0,"syntology":null},{"paper":null,"slug":"rotational-rectification-network-enabling","title":"Rotational Rectification Network: Enabling Pedestrian Detection for Mobile Vision","date":"2017-06-19","arxiv_id":"1706.08917","n_code_links":0,"syntology":null},{"paper":null,"slug":"hardware-efficient-on-line-learning-through","title":"Hardware-efficient on-line learning through pipelined truncated-error backpropagation in binary-state networks","date":"2017-06-15","arxiv_id":"1707.03049","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-grounding-conceptual-spaces-in-neural","title":"Towards Grounding Conceptual Spaces in Neural Representations","date":"2017-06-15","arxiv_id":"1706.04825","n_code_links":0,"syntology":null},{"paper":null,"slug":"sep-nets-small-and-effective-pattern-networks","title":"SEP-Nets: Small and Effective Pattern Networks","date":"2017-06-13","arxiv_id":"1706.03912","n_code_links":0,"syntology":null},{"paper":"/paper/attention-is-all-you-need","slug":"attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","arxiv_id":"1706.03762","n_code_links":595,"syntology":{"ran":610,"of":946,"n_ran_checked":529,"n_instrument":81,"unverified":336,"pointer_only":451,"phrase":"610 ran (of which 293 constructed an object rather than computing a result; 529 with no instrument failure: 45 honoured, 15 violated, 469 with no contract checked; 81 where Syntology's instrument failed) · 336 unverified","official":{"repos":["tensorflow/tensor2tensor"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"paper":null,"slug":"enriched-deep-recurrent-visual-attention","title":"Enriched Deep Recurrent Visual Attention Model for Multiple Object Recognition","date":"2017-06-12","arxiv_id":"1706.03581","n_code_links":0,"syntology":null},{"paper":"/paper/advances-in-joint-ctc-attention-based-end-to","slug":"advances-in-joint-ctc-attention-based-end-to","title":"Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM","date":"2017-06-08","arxiv_id":"1706.02737","n_code_links":6,"syntology":null},{"paper":"/paper/enhancing-the-reliability-of-out-of","slug":"enhancing-the-reliability-of-out-of","title":"Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks","date":"2017-06-08","arxiv_id":"1706.02690","n_code_links":9,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["facebookresearch/odin"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/multi-agent-actor-critic-for-mixed","slug":"multi-agent-actor-critic-for-mixed","title":"Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments","date":"2017-06-07","arxiv_id":"1706.02275","n_code_links":86,"syntology":{"ran":75,"of":143,"n_ran_checked":68,"n_instrument":7,"unverified":68,"pointer_only":99,"phrase":"75 ran (of which 54 constructed an object rather than computing a result; 68 with no instrument failure: 2 honoured, 0 violated, 66 with no contract checked; 7 where Syntology's instrument failed) · 68 unverified","official":{"repos":["openai/multiagent-particle-envs"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"paper":"/paper/parameter-space-noise-for-exploration","slug":"parameter-space-noise-for-exploration","title":"Parameter Space Noise for Exploration","date":"2017-06-06","arxiv_id":"1706.01905","n_code_links":10,"syntology":{"ran":4,"of":5,"n_ran_checked":2,"n_instrument":2,"unverified":1,"pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/deep-generative-adversarial-networks-for","slug":"deep-generative-adversarial-networks-for","title":"Deep Generative Adversarial Networks for Compressed Sensing Automates MRI","date":"2017-05-31","arxiv_id":"1706.00051","n_code_links":2,"syntology":null},{"paper":"/paper/discriminatively-learned-hierarchical-rank","slug":"discriminatively-learned-hierarchical-rank","title":"Discriminatively Learned Hierarchical Rank Pooling Networks","date":"2017-05-30","arxiv_id":"1705.10420","n_code_links":1,"syntology":null},{"paper":"/paper/rsi-cb-a-large-scale-remote-sensing-image","slug":"rsi-cb-a-large-scale-remote-sensing-image","title":"RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data","date":"2017-05-30","arxiv_id":"1705.10450","n_code_links":1,"syntology":null},{"paper":"/paper/deep-voice-2-multi-speaker-neural-text-to","slug":"deep-voice-2-multi-speaker-neural-text-to","title":"Deep Voice 2: Multi-Speaker Neural Text-to-Speech","date":"2017-05-24","arxiv_id":"1705.08947","n_code_links":1,"syntology":null},{"paper":null,"slug":"explaining-transition-systems-through-program","title":"Explaining Transition Systems through Program Induction","date":"2017-05-23","arxiv_id":"1705.08320","n_code_links":0,"syntology":null},{"paper":"/paper/terngrad-ternary-gradients-to-reduce","slug":"terngrad-ternary-gradients-to-reduce","title":"TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning","date":"2017-05-22","arxiv_id":"1705.07878","n_code_links":1,"syntology":null},{"paper":null,"slug":"shallow-updates-for-deep-reinforcement","title":"Shallow Updates for Deep Reinforcement Learning","date":"2017-05-21","arxiv_id":"1705.07461","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-factor-policies-and-action-value","title":"Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning","date":"2017-05-20","arxiv_id":"1705.07269","n_code_links":0,"syntology":null},{"paper":"/paper/feature-control-as-intrinsic-motivation-for","slug":"feature-control-as-intrinsic-motivation-for","title":"Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning","date":"2017-05-18","arxiv_id":"1705.06769","n_code_links":1,"syntology":null},{"paper":null,"slug":"design-of-a-very-compact-cnn-classifier-for","title":"Design of a Very Compact CNN Classifier for Online Handwritten Chinese Character Recognition Using DropWeight and Global Pooling","date":"2017-05-15","arxiv_id":"1705.05207","n_code_links":0,"syntology":null},{"paper":null,"slug":"discrete-sequential-prediction-of-continuous","title":"Discrete Sequential Prediction of Continuous Actions for Deep RL","date":"2017-05-14","arxiv_id":"1705.05035","n_code_links":0,"syntology":null},{"paper":null,"slug":"gabor-filter-assisted-energy-efficient-fast","title":"Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks","date":"2017-05-12","arxiv_id":"1705.04748","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-and-scalable-view-generation-from-a","title":"Efficient and Scalable View Generation from a Single Image using Fully Convolutional Networks","date":"2017-05-10","arxiv_id":"1705.03737","n_code_links":0,"syntology":null},{"paper":null,"slug":"ternary-neural-networks-with-fine-grained","title":"Ternary Neural Networks with Fine-Grained Quantization","date":"2017-05-02","arxiv_id":"1705.01462","n_code_links":0,"syntology":null},{"paper":"/paper/beta-vae-learning-basic-visual-concepts-with","slug":"beta-vae-learning-basic-visual-concepts-with","title":"beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework","date":"2017-04-26","arxiv_id":null,"n_code_links":6,"syntology":null},{"paper":null,"slug":"equivalence-between-policy-gradients-and-soft","title":"Equivalence Between Policy Gradients and Soft Q-Learning","date":"2017-04-21","arxiv_id":"1704.06440","n_code_links":0,"syntology":null},{"paper":"/paper/accurate-single-stage-detector-using","slug":"accurate-single-stage-detector-using","title":"Accurate Single Stage Detector Using Recurrent Rolling Convolution","date":"2017-04-19","arxiv_id":"1704.05776","n_code_links":2,"syntology":null},{"paper":"/paper/mobilenets-efficient-convolutional-neural","slug":"mobilenets-efficient-convolutional-neural","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","date":"2017-04-17","arxiv_id":"1704.04861","n_code_links":159,"syntology":{"ran":53,"of":83,"n_ran_checked":44,"n_instrument":9,"unverified":30,"pointer_only":48,"phrase":"53 ran (of which 28 constructed an object rather than computing a result; 44 with no instrument failure: 4 honoured, 0 violated, 40 with no contract checked; 9 where Syntology's instrument failed) · 30 unverified","official":{"repos":["tensorflow/tensorflow"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"paper":"/paper/deep-learning-for-decentralized-parking-lot","slug":"deep-learning-for-decentralized-parking-lot","title":"Deep learning for decentralized parking lot occupancy detection","date":"2017-04-15","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/the-reactor-a-fast-and-sample-efficient-actor","slug":"the-reactor-a-fast-and-sample-efficient-actor","title":"The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning","date":"2017-04-15","arxiv_id":"1704.04651","n_code_links":0,"syntology":null},{"paper":"/paper/deep-q-learning-from-demonstrations","slug":"deep-q-learning-from-demonstrations","title":"Deep Q-learning from Demonstrations","date":"2017-04-12","arxiv_id":"1704.03732","n_code_links":6,"syntology":null},{"paper":"/paper/cutting-the-error-by-half-investigation-of","slug":"cutting-the-error-by-half-investigation-of","title":"Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification","date":"2017-04-11","arxiv_id":"1704.03557","n_code_links":5,"syntology":null},{"paper":null,"slug":"uc-merced-submission-to-the-activitynet","title":"UC Merced Submission to the ActivityNet Challenge 2016","date":"2017-04-11","arxiv_id":"1704.03503","n_code_links":0,"syntology":null},{"paper":null,"slug":"evolution-in-groups-a-deeper-look-at-synaptic","title":"Evolution in Groups: A deeper look at synaptic cluster driven evolution of deep neural networks","date":"2017-04-07","arxiv_id":"1704.02081","n_code_links":0,"syntology":null},{"paper":null,"slug":"dyvedeep-dynamic-variable-effort-deep-neural","title":"DyVEDeep: Dynamic Variable Effort Deep Neural Networks","date":"2017-04-04","arxiv_id":"1704.01137","n_code_links":0,"syntology":null},{"paper":"/paper/snapshot-ensembles-train-1-get-m-for-free","slug":"snapshot-ensembles-train-1-get-m-for-free","title":"Snapshot Ensembles: Train 1, get M for free","date":"2017-04-01","arxiv_id":"1704.00109","n_code_links":11,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["gaohuang/SnapshotEnsemble"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"transfer-of-view-manifold-learning-to","title":"Transfer of View-manifold Learning to Similarity Perception of Novel Objects","date":"2017-03-31","arxiv_id":"1704.00033","n_code_links":0,"syntology":null},{"paper":"/paper/tacotron-towards-end-to-end-speech-synthesis","slug":"tacotron-towards-end-to-end-speech-synthesis","title":"Tacotron: Towards End-to-End Speech Synthesis","date":"2017-03-29","arxiv_id":"1703.10135","n_code_links":30,"syntology":{"ran":16,"of":25,"n_ran_checked":13,"n_instrument":3,"unverified":9,"pointer_only":6,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 3 honoured, 1 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 9 unverified","official":null}},{"paper":"/paper/coordinating-filters-for-faster-deep-neural","slug":"coordinating-filters-for-faster-deep-neural","title":"Coordinating Filters for Faster Deep Neural Networks","date":"2017-03-28","arxiv_id":"1703.09746","n_code_links":5,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["wenwei202/caffe"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"theory-ii-landscape-of-the-empirical-risk-in","title":"Theory II: Landscape of the Empirical Risk in Deep Learning","date":"2017-03-28","arxiv_id":"1703.09833","n_code_links":0,"syntology":null},{"paper":"/paper/active-convolution-learning-the-shape-of","slug":"active-convolution-learning-the-shape-of","title":"Active Convolution: Learning the Shape of Convolution for Image Classification","date":"2017-03-27","arxiv_id":"1703.09076","n_code_links":1,"syntology":null},{"paper":"/paper/is-second-order-information-helpful-for-large","slug":"is-second-order-information-helpful-for-large","title":"Is Second-order Information Helpful for Large-scale Visual Recognition?","date":"2017-03-23","arxiv_id":"1703.08050","n_code_links":1,"syntology":null},{"paper":"/paper/on-the-robustness-of-convolutional-neural","slug":"on-the-robustness-of-convolutional-neural","title":"On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations","date":"2017-03-23","arxiv_id":"1703.08245","n_code_links":1,"syntology":null},{"paper":"/paper/arbitrary-style-transfer-in-real-time-with","slug":"arbitrary-style-transfer-in-real-time-with","title":"Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization","date":"2017-03-20","arxiv_id":"1703.06868","n_code_links":29,"syntology":{"ran":32,"of":41,"n_ran_checked":16,"n_instrument":16,"unverified":9,"pointer_only":29,"phrase":"32 ran (of which 4 constructed an object rather than computing a result; 16 with no instrument failure: 3 honoured, 0 violated, 13 with no contract checked; 16 where Syntology's instrument failed) · 9 unverified","official":{"repos":["xunhuang1995/AdaIN-style"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"multilevel-context-representation-for","title":"Multilevel Context Representation for Improving Object Recognition","date":"2017-03-19","arxiv_id":"1703.06408","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-hybrid-supervised-unsupervised-method-on","title":"A Hybrid Supervised-unsupervised Method on Image Topic Visualization with Convolutional Neural Network and LDA","date":"2017-03-15","arxiv_id":"1703.05243","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-for-skin-lesion-classification","title":"Deep Learning for Skin Lesion Classification","date":"2017-03-13","arxiv_id":"1703.04364","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-compact-dnn-approaching-googlenet-level","title":"A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation","date":"2017-03-12","arxiv_id":"1703.04071","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-convolutional-neural-network-inference","title":"Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations","date":"2017-03-08","arxiv_id":"1703.03073","n_code_links":0,"syntology":null},{"paper":null,"slug":"tactics-of-adversarial-attack-on-deep","title":"Tactics of Adversarial Attack on Deep Reinforcement Learning Agents","date":"2017-03-08","arxiv_id":"1703.06748","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-limits-of-learning-representations","title":"On the Limits of Learning Representations with Label-Based Supervision","date":"2017-03-07","arxiv_id":"1703.02156","n_code_links":0,"syntology":null},{"paper":null,"slug":"chain-nn-an-energy-efficient-1d-chain","title":"Chain-NN: An Energy-Efficient 1D Chain Architecture for Accelerating Deep Convolutional Neural Networks","date":"2017-03-04","arxiv_id":"1703.01457","n_code_links":0,"syntology":null},{"paper":null,"slug":"skin-lesion-classification-using-deep-multi","title":"Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks","date":"2017-03-04","arxiv_id":"1703.01402","n_code_links":0,"syntology":null},{"paper":"/paper/count-based-exploration-with-neural-density","slug":"count-based-exploration-with-neural-density","title":"Count-Based Exploration with Neural Density Models","date":"2017-03-03","arxiv_id":"1703.01310","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"deep-collaborative-learning-for-visual","title":"Deep Collaborative Learning for Visual Recognition","date":"2017-03-03","arxiv_id":"1703.01229","n_code_links":0,"syntology":null},{"paper":null,"slug":"araguaia-medical-vision-lab-at-isic-2017-skin","title":"Araguaia Medical Vision Lab at ISIC 2017 Skin Lesion Classification Challenge","date":"2017-03-02","arxiv_id":"1703.00856","n_code_links":0,"syntology":null},{"paper":"/paper/enabling-sparse-winograd-convolution-by","slug":"enabling-sparse-winograd-convolution-by","title":"Enabling Sparse Winograd Convolution by Native Pruning","date":"2017-02-28","arxiv_id":"1702.08597","n_code_links":1,"syntology":null},{"paper":"/paper/learning-deep-visual-object-models-from-noisy","slug":"learning-deep-visual-object-models-from-noisy","title":"Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work","date":"2017-02-28","arxiv_id":"1702.08513","n_code_links":1,"syntology":null},{"paper":null,"slug":"skin-lesion-classification-using-hybrid-deep","title":"Skin Lesion Classification Using Hybrid Deep Neural Networks","date":"2017-02-27","arxiv_id":"1702.08434","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-power-of-sparsity-in-convolutional-neural","title":"The Power of Sparsity in Convolutional Neural Networks","date":"2017-02-21","arxiv_id":"1702.06257","n_code_links":0,"syntology":null},{"paper":null,"slug":"group-scissor-scaling-neuromorphic-computing","title":"Group Scissor: Scaling Neuromorphic Computing Design to Large Neural Networks","date":"2017-02-11","arxiv_id":"1702.03443","n_code_links":0,"syntology":null},{"paper":"/paper/incremental-network-quantization-towards","slug":"incremental-network-quantization-towards","title":"Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights","date":"2017-02-10","arxiv_id":"1702.03044","n_code_links":3,"syntology":null},{"paper":"/paper/sigmoid-weighted-linear-units-for-neural","slug":"sigmoid-weighted-linear-units-for-neural","title":"Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning","date":"2017-02-10","arxiv_id":"1702.03118","n_code_links":0,"syntology":null},{"paper":"/paper/autonomous-braking-system-via-deep","slug":"autonomous-braking-system-via-deep","title":"Autonomous Braking System via Deep Reinforcement Learning","date":"2017-02-08","arxiv_id":"1702.02302","n_code_links":2,"syntology":null},{"paper":"/paper/deep-learning-with-low-precision-by-half-wave","slug":"deep-learning-with-low-precision-by-half-wave","title":"Deep Learning with Low Precision by Half-wave Gaussian Quantization","date":"2017-02-03","arxiv_id":"1702.00953","n_code_links":1,"syntology":null},{"paper":null,"slug":"information-theoretic-interpretation-of","title":"Information-theoretic interpretation of tuning curves for multiple motion directions","date":"2017-02-01","arxiv_id":"1702.00493","n_code_links":0,"syntology":null},{"paper":"/paper/variational-dropout-sparsifies-deep-neural","slug":"variational-dropout-sparsifies-deep-neural","title":"Variational Dropout Sparsifies Deep Neural Networks","date":"2017-01-19","arxiv_id":"1701.05369","n_code_links":15,"syntology":{"ran":1,"of":3,"n_ran_checked":1,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":null,"slug":"image-generation-and-editing-with-variational","title":"Image Generation and Editing with Variational Info Generative AdversarialNetworks","date":"2017-01-17","arxiv_id":"1701.04568","n_code_links":0,"syntology":null},{"paper":"/paper/vulnerability-of-deep-reinforcement-learning","slug":"vulnerability-of-deep-reinforcement-learning","title":"Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks","date":"2017-01-16","arxiv_id":"1701.04143","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-opencltm-deep-learning-accelerator-on","title":"An OpenCL(TM) Deep Learning Accelerator on Arria 10","date":"2017-01-13","arxiv_id":"1701.03534","n_code_links":0,"syntology":null},{"paper":null,"slug":"identifying-best-interventions-through-online","title":"Identifying Best Interventions through Online Importance Sampling","date":"2017-01-10","arxiv_id":"1701.02789","n_code_links":0,"syntology":null},{"paper":"/paper/oriented-response-networks","slug":"oriented-response-networks","title":"Oriented Response Networks","date":"2017-01-07","arxiv_id":"1701.01833","n_code_links":1,"syntology":null},{"paper":null,"slug":"adversarially-tuned-scene-generation","title":"Adversarially Tuned Scene Generation","date":"2017-01-02","arxiv_id":"1701.00405","n_code_links":0,"syntology":null},{"paper":null,"slug":"collaborative-creativity-with-monte-carlo","title":"Collaborative creativity with Monte-Carlo Tree Search and Convolutional Neural Networks","date":"2016-12-14","arxiv_id":"1612.04876","n_code_links":0,"syntology":null},{"paper":"/paper/fast-patch-based-style-transfer-of-arbitrary","slug":"fast-patch-based-style-transfer-of-arbitrary","title":"Fast Patch-based Style Transfer of Arbitrary Style","date":"2016-12-13","arxiv_id":"1612.04337","n_code_links":6,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["rtqichen/style-swap"],"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/imagenet-pre-trained-models-with-batch","slug":"imagenet-pre-trained-models-with-batch","title":"ImageNet pre-trained models with batch normalization","date":"2016-12-05","arxiv_id":"1612.01452","n_code_links":4,"syntology":null},{"paper":null,"slug":"towards-the-limit-of-network-quantization","title":"Towards the Limit of Network Quantization","date":"2016-12-05","arxiv_id":"1612.01543","n_code_links":0,"syntology":null},{"paper":"/paper/learning-to-segment-object-candidates-via","slug":"learning-to-segment-object-candidates-via","title":"Learning to Segment Object Candidates via Recursive Neural Networks","date":"2016-12-04","arxiv_id":"1612.01057","n_code_links":0,"syntology":null},{"paper":"/paper/trained-ternary-quantization","slug":"trained-ternary-quantization","title":"Trained Ternary Quantization","date":"2016-12-04","arxiv_id":"1612.01064","n_code_links":6,"syntology":{"ran":5,"of":7,"n_ran_checked":1,"n_instrument":4,"unverified":2,"pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":null,"slug":"in-teacher-we-trust-learning-compressed","title":"In Teacher We Trust: Learning Compressed Models for Pedestrian Detection","date":"2016-12-01","arxiv_id":"1612.00478","n_code_links":0,"syntology":null},{"paper":"/paper/speedaccuracy-trade-offs-for-modern","slug":"speedaccuracy-trade-offs-for-modern","title":"Speed/accuracy trade-offs for modern convolutional object detectors","date":"2016-11-30","arxiv_id":"1611.10012","n_code_links":14,"syntology":null},{"paper":"/paper/deep-reinforcement-learning-for-multi-domain","slug":"deep-reinforcement-learning-for-multi-domain","title":"Deep Reinforcement Learning for Multi-Domain Dialogue Systems","date":"2016-11-26","arxiv_id":"1611.08675","n_code_links":1,"syntology":null},{"paper":null,"slug":"memory-lens-how-much-memory-does-an-agent-use","title":"Memory Lens: How Much Memory Does an Agent Use?","date":"2016-11-21","arxiv_id":"1611.06928","n_code_links":0,"syntology":null},{"paper":"/paper/lcnn-lookup-based-convolutional-neural","slug":"lcnn-lookup-based-convolutional-neural","title":"LCNN: Lookup-based Convolutional Neural Network","date":"2016-11-20","arxiv_id":"1611.06473","n_code_links":2,"syntology":null},{"paper":null,"slug":"lots-about-attacking-deep-features","title":"LOTS about Attacking Deep Features","date":"2016-11-18","arxiv_id":"1611.06179","n_code_links":0,"syntology":null},{"paper":"/paper/reinforcement-learning-through-asynchronous","slug":"reinforcement-learning-through-asynchronous","title":"Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU","date":"2016-11-18","arxiv_id":"1611.06256","n_code_links":3,"syntology":null},{"paper":null,"slug":"weakly-supervised-learning-of-mid-level","title":"Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization","date":"2016-11-17","arxiv_id":"1611.05603","n_code_links":0,"syntology":null},{"paper":null,"slug":"designing-energy-efficient-convolutional","title":"Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning","date":"2016-11-16","arxiv_id":"1611.05128","n_code_links":0,"syntology":null},{"paper":"/paper/least-squares-generative-adversarial-networks","slug":"least-squares-generative-adversarial-networks","title":"Least Squares Generative Adversarial Networks","date":"2016-11-13","arxiv_id":"1611.04076","n_code_links":24,"syntology":{"ran":8,"of":9,"n_ran_checked":7,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 3 honoured, 1 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["xudonmao/LSGAN"],"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":"gradients-of-counterfactuals","title":"Gradients of Counterfactuals","date":"2016-11-08","arxiv_id":"1611.02639","n_code_links":0,"syntology":null},{"paper":null,"slug":"averaged-dqn-variance-reduction-and","title":"Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning","date":"2016-11-07","arxiv_id":"1611.01929","n_code_links":0,"syntology":null},{"paper":null,"slug":"boosting-image-captioning-with-attributes","title":"Boosting Image Captioning with Attributes","date":"2016-11-05","arxiv_id":"1611.01646","n_code_links":0,"syntology":null},{"paper":"/paper/sample-efficient-actor-critic-with-experience","slug":"sample-efficient-actor-critic-with-experience","title":"Sample Efficient Actor-Critic with Experience Replay","date":"2016-11-03","arxiv_id":"1611.01224","n_code_links":7,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":null}},{"paper":"/paper/deep-convolutional-neural-network-design","slug":"deep-convolutional-neural-network-design","title":"Deep Convolutional Neural Network Design Patterns","date":"2016-11-02","arxiv_id":"1611.00847","n_code_links":1,"syntology":null},{"paper":null,"slug":"fine-grained-recognition-in-the-noisy-wild","title":"Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches","date":"2016-10-21","arxiv_id":"1610.06756","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-training-of-convolutional-neural","title":"Fast Training of Convolutional Neural Networks via Kernel Rescaling","date":"2016-10-12","arxiv_id":"1610.03623","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-reinforcement-learning-from-raw-pixels","title":"Deep Reinforcement Learning From Raw Pixels in Doom","date":"2016-10-07","arxiv_id":"1610.02164","n_code_links":0,"syntology":null},{"paper":"/paper/ilgnet-inception-modules-with-connected-local","slug":"ilgnet-inception-modules-with-connected-local","title":"ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain Adaptation","date":"2016-10-07","arxiv_id":"1610.02256","n_code_links":2,"syntology":null}],"record_sha256":"88a3c95145aaaf98f4bdc95732bf17f4e9f5e4f223a7d56fdc05423a1f7797ff","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}