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Why does deep and cheap learning work so well?

29 August 2016

Papers citing "Why does deep and cheap learning work so well?"

50 / 236 papers shown

Title
Maximum Multiscale Entropy and Neural Network Regularization Amir-Reza Asadi Emmanuel Abbe 92 1 0 25 Jun 2020
Predicting First Passage Percolation Shapes Using Neural Networks Sebastian Rosengren AI4CE 54 0 0 24 Jun 2020
Hierarchically Compositional Tasks and Deep Convolutional NetworksJournal of Vision (J Vis), 2020 Arturo Deza Q. Liao Andrzej Banburski T. Poggio BDL OOD 183 2 0 24 Jun 2020
Restricted Boltzmann Machine Flows and The Critical Temperature of Ising models R. Veiga R. Vicente AI4CE 117 5 0 17 Jun 2020
PAC-Bayesian Generalization Bounds for MultiLayer Perceptrons Xinjie Lan Xin Guo Kenneth Barner 175 3 0 16 Jun 2020
Minimum Width for Universal Approximation Sejun Park Chulhee Yun Jaeho Lee Jinwoo Shin 159 138 0 16 Jun 2020
Tangent Space Sensitivity and Distribution of Linear Regions in ReLU Networks Balint Daroczy AAML 91 0 0 11 Jun 2020
Machine Learning for Condensed Matter Physics Edwin Bedolla L. C. Padierna R. Castañeda-Priego AI4CE 235 83 0 28 May 2020
PDE constraints on smooth hierarchical functions computed by neural networks Khashayar Filom Konrad Paul Kording Roozbeh Farhoodi 103 0 0 18 May 2020
How hard is to distinguish graphs with graph neural networks? Andreas Loukas GNN 235 6 0 13 May 2020
Boosting on the shoulders of giants in quantum device calibration A. Wozniakowski Jayne Thompson M. Gu F. Binder 130 4 0 13 May 2020
Off-the-shelf deep learning is not enough: parsimony, Bayes and causality Rama K Vasudevan M. Ziatdinov L. Vlček Sergei V. Kalinin BDL CML AI4CE 60 0 0 04 May 2020
Bias-corrected estimator for intrinsic dimension and differential entropy--a visual multiscale approachJournal of Communication and Information Systems (JCIS), 2020 J. Filho J. Canuto Luiz Miranda 81 0 0 30 Apr 2020
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens BDL 441 188 0 23 Apr 2020
Towards a theory of machine learning V. Vanchurin 194 27 0 15 Apr 2020
Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement Learning Jeffrey M. Ede 727 14 0 06 Apr 2020
Depth Selection for Deep ReLU Nets in Feature Extraction and GeneralizationIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020 Zhi Han Siquan Yu Shao-Bo Lin Ding-Xuan Zhou OOD 128 45 0 01 Apr 2020
Comments on Sejnowski's "The unreasonable effectiveness of deep learning in artificial intelligence" [arXiv:2002.04806] L. Smith 53 0 0 20 Mar 2020
Warwick Electron Microscopy Datasets Jeffrey M. Ede 225 14 0 02 Mar 2020
A closer look at the approximation capabilities of neural networksInternational Conference on Learning Representations (ICLR), 2020 Kai Fong Ernest Chong 64 17 0 16 Feb 2020
Neural network wave functions and the sign problemPhysical Review Research (PRResearch), 2020 A. Szabó C. Castelnovo 164 80 0 11 Feb 2020
Short sighted deep learningPhysical Review E (PRE), 2020 R. Koch Anita de Mello Koch Nicholas Kastanos Ling Cheng 143 8 0 07 Feb 2020
Differentiable programming and its applications to dynamical systems A. Hernández José M. Amigó 162 11 0 17 Dec 2019
Realization of spatial sparseness by deep ReLU nets with massive dataIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019 C. Chui Shao-Bo Lin Bo Zhang Ding-Xuan Zhou 106 24 0 16 Dec 2019
Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks Héctor J. Hortúa Riccardo Volpi D. Marinelli Luigi Malagò BDL 209 25 0 19 Nov 2019
Generalization in multitask deep neural classifiers: a statistical physics approachNeural Information Processing Systems (NeurIPS), 2019 Tyler Lee A. Ndirango AI4CE 245 20 0 30 Oct 2019
CTNN: Corticothalamic-inspired neural network Leendert A. Remmelzwaal A. Mishra George F. R. Ellis OOD 162 1 0 28 Oct 2019
Face representation by deep learning: a linear encoding in a parameter space? Qiulei Dong Qiulei Dong Zhanyi Hu CVBM 181 1 0 22 Oct 2019
A Flexible Framework for Anomaly Detection via Dimensionality Reduction A. V. Sadr Bruce A. Bassett M. Kunz 126 15 0 09 Sep 2019
Sparse hierarchical representation learning on molecular graphs M. Bal Hagen Triendl Mariana Assmann M. Craig Lawrence Phillips J. Frost Usman Bashir Noor Shaker V. Stojevic GNN 95 1 0 06 Aug 2019
A direct approach for function approximation on data defined manifolds H. Mhaskar 74 1 0 01 Aug 2019
Convolutional Neural Networks on Randomized Data Cristian Ivan 84 14 0 25 Jul 2019
A Group-Theoretic Framework for Data Augmentation Shuxiao Chen Edgar Dobriban Jane Lee FedML 207 38 0 25 Jul 2019
Post-synaptic potential regularization has potentialInternational Conference on Artificial Neural Networks (ICANN), 2019 Enzo Tartaglione Daniele Perlo Marco Grangetto BDL AAML 116 6 0 19 Jul 2019
Privileged Features Distillation at Taobao RecommendationsKnowledge Discovery and Data Mining (KDD), 2019 Chen Xu Quan Li Junfeng Ge Jinyang Gao Xiaoyong Yang Changhua Pei Fei Sun Jian Wu Hanxiao Sun Wenwu Ou 162 73 0 11 Jul 2019
Parameterized quantum circuits as machine learning modelsQuantum Science and Technology (QST), 2019 Marcello Benedetti Erika Lloyd Stefan H. Sack Mattia Fiorentini 432 1,061 0 18 Jun 2019
Interpretations of Deep Learning by Forests and Haar Wavelets Changcun Huang FAtt 127 0 0 16 Jun 2019
Is Deep Learning a Renormalization Group Flow? E. Koch R. Koch Ling Cheng OOD AI4CE 141 5 0 12 Jun 2019
When and Why Metaheuristics Researchers Can Ignore "No Free Lunch" Theorems James McDermott FedML 98 17 0 07 Jun 2019
Deep ReLU Networks Have Surprisingly Few Activation PatternsNeural Information Processing Systems (NeurIPS), 2019 Boris Hanin David Rolnick 386 247 0 03 Jun 2019
Partial Scanning Transmission Electron Microscopy with Deep Learning Jeffrey M. Ede R. Beanland MedIm 245 22 0 31 May 2019
Provably scale-covariant continuous hierarchical networks based on scale-normalized differential expressions coupled in cascadeJournal of Mathematical Imaging and Vision (JMIV), 2019 T. Lindeberg 232 21 0 29 May 2019
AI Feynman: a Physics-Inspired Method for Symbolic RegressionScience Advances (Sci Adv), 2019 S. Udrescu Max Tegmark 423 1,056 0 27 May 2019
On the descriptive power of Neural-Networks as constrained Tensor Networks with exponentially large bond dimensionSciPost Physics Core (SPC), 2019 M. Collura L. Dell’Anna Timo Felser S. Montangero 154 21 0 27 May 2019
A Selective Overview of Deep Learning Jianqing Fan Cong Ma Yiqiao Zhong BDL VLM 367 145 0 10 Apr 2019
On functions computed on trees Roozbeh Farhoodi Khashayar Filom I. Jones Konrad Paul Kording PINN 164 5 0 04 Apr 2019
Deep Neural Networks for Rotation-Invariance Approximation and Learning C. Chui Shao-Bo Lin Ding-Xuan Zhou 218 36 0 03 Apr 2019
Generative Tensor Network Classification Model for Supervised Machine Learning Zheng-Zhi Sun C. Peng Ding Liu Shi-Ju Ran G. Su 94 48 0 26 Mar 2019
ToyArchitecture: Unsupervised Learning of Interpretable Models of the World Jaroslav Vítků Petr Dluhos Joseph Davidson Matej Nikl Simon Andersson ... Martin Stránský M. Hyben Martin Poliak Jan Feyereisl Marek Rosa AI4CE SSL 234 0 0 20 Mar 2019
How to Make Swarms Open-Ended? Evolving Collective Intelligence Through a Constricted Exploration of Adjacent PossiblesArtificial Life (ALIFE), 2019 Olaf Witkowski T. Ikegami 101 8 0 19 Mar 2019