Why does deep and cheap learning work so well?

29 August 2016

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

50 / 76 papers shown

Title
A Mathematical Philosophy of Explanations in Mechanistic Interpretability -- The Strange Science Part I.i Kola Ayonrinde Louis Jaburi MILM 88 1 0 01 May 2025
Multilevel Generative Samplers for Investigating Critical Phenomena Ankur Singha E. Cellini K. Nicoli K. Jansen Stefan Kühn Shinichi Nakajima 64 1 0 11 Mar 2025
KAN: Kolmogorov-Arnold Networks Ziming Liu Yixuan Wang Sachin Vaidya Fabian Ruehle James Halverson Marin Soljacic Thomas Y. Hou Max Tegmark 98 475 0 30 Apr 2024
Extracting Formulae in Many-Valued Logic from Deep Neural Networks Yani Zhang Helmut Bölcskei 24 0 0 22 Jan 2024
Autonomous Learning of Generative Models with Chemical Reaction Network Ensembles William G. Poole T. Ouldridge Manoj Gopalkrishnan 13 2 0 02 Nov 2023
Deep Neural Networks for Automatic Speaker Recognition Do Not Learn Supra-Segmental Temporal Features Daniel Neururer Volker Dellwo Thilo Stadelmann 36 2 0 01 Nov 2023
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss T. Getu Georges Kaddoum M. Bennis 40 1 0 13 Sep 2023
Iterative Magnitude Pruning as a Renormalisation Group: A Study in The Context of The Lottery Ticket Hypothesis Abu-Al Hassan 33 0 0 06 Aug 2023
Spherical and Hyperbolic Toric Topology-Based Codes On Graph Embedding for Ising MRF Models: Classical and Quantum Topology Machine Learning V. Usatyuk Sergey Egorov Denis Sapozhnikov 29 3 0 28 Jul 2023
Bayesian Renormalization D. Berman Marc S. Klinger A. G. Stapleton 38 16 0 17 May 2023
Do deep neural networks have an inbuilt Occam's razor? Chris Mingard Henry Rees Guillermo Valle Pérez A. Louis UQCV BDL 21 16 0 13 Apr 2023
GenPhys: From Physical Processes to Generative Models Ziming Liu Di Luo Yilun Xu Tommi Jaakkola M. Tegmark AI4CE 24 14 0 05 Apr 2023
From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding Borui Cai Yong Xiang Longxiang Gao Di Wu Heng Zhang Jiongdao Jin Tom H. Luan 29 1 0 22 Mar 2023
MOSAIC, acomparison framework for machine learning models Mattéo Papin Yann Beaujeault-Taudiere F. Magniette VLM 18 0 0 30 Jan 2023
Renormalization in the neural network-quantum field theory correspondence Harold Erbin Vincent Lahoche D. O. Samary 39 7 0 22 Dec 2022
Changes from Classical Statistics to Modern Statistics and Data Science Kai Zhang Shan-Yu Liu M. Xiong 34 0 0 30 Oct 2022
Precision Machine Learning Eric J. Michaud Ziming Liu Max Tegmark 24 34 0 24 Oct 2022
When Expressivity Meets Trainability: Fewer than $n$ Neurons Can Work Jiawei Zhang Yushun Zhang Mingyi Hong Ruoyu Sun Zhi-Quan Luo 29 10 0 21 Oct 2022
Why neural networks find simple solutions: the many regularizers of geometric complexity Benoit Dherin Michael Munn M. Rosca David Barrett 57 31 0 27 Sep 2022
Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation Jeffmin Lin Gil Goldshlager Lin Lin 43 22 0 07 Dec 2021
Impossibility Results in AI: A Survey Mario Brčič Roman V. Yampolskiy 26 25 0 01 Sep 2021
Towards quantifying information flows: relative entropy in deep neural networks and the renormalization group J. Erdmenger Kevin T. Grosvenor R. Jefferson 54 17 0 14 Jul 2021
On the approximation of functions by tanh neural networks Tim De Ryck S. Lanthaler Siddhartha Mishra 26 138 0 18 Apr 2021
Tensor networks and efficient descriptions of classical data Sirui Lu Márton Kanász-Nagy I. Kukuljan J. I. Cirac 24 24 0 11 Mar 2021
Deep ReLU Networks Preserve Expected Length Boris Hanin Ryan Jeong David Rolnick 29 14 0 21 Feb 2021
Advances in Electron Microscopy with Deep Learning Jeffrey M. Ede 35 2 0 04 Jan 2021
Physics-Based Deep Learning for Fiber-Optic Communication Systems Christian Hager H. Pfister 24 66 0 27 Oct 2020
Review: Deep Learning in Electron Microscopy Jeffrey M. Ede 34 79 0 17 Sep 2020
On Representing (Anti)Symmetric Functions Marcus Hutter 17 22 0 30 Jul 2020
Modeling Generalization in Machine Learning: A Methodological and Computational Study Pietro Barbiero Giovanni Squillero Alberto Tonda 10 35 0 28 Jun 2020
Thermodynamic Machine Learning through Maximum Work Production A. B. Boyd James P. Crutchfield M. Gu AI4CE 24 16 0 27 Jun 2020
Predicting First Passage Percolation Shapes Using Neural Networks Sebastian Rosengren AI4CE 12 0 0 24 Jun 2020
Hierarchically Compositional Tasks and Deep Convolutional Networks Arturo Deza Q. Liao Andrzej Banburski T. Poggio BDL OOD 25 2 0 24 Jun 2020
Minimum Width for Universal Approximation Sejun Park Chulhee Yun Jaeho Lee Jinwoo Shin 33 121 0 16 Jun 2020
How hard is to distinguish graphs with graph neural networks? Andreas Loukas GNN 25 6 0 13 May 2020
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens BDL 44 172 0 23 Apr 2020
Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement Learning Jeffrey M. Ede 24 12 0 06 Apr 2020
Warwick Electron Microscopy Datasets Jeffrey M. Ede 22 14 0 02 Mar 2020
A closer look at the approximation capabilities of neural networks Kai Fong Ernest Chong 21 16 0 16 Feb 2020
Neural network wave functions and the sign problem A. Szabó C. Castelnovo 21 69 0 11 Feb 2020
Face representation by deep learning: a linear encoding in a parameter space? Qiulei Dong Jiaying Sun Zhanyi Hu CVBM 17 1 0 22 Oct 2019
Post-synaptic potential regularization has potential Enzo Tartaglione Daniele Perlo Marco Grangetto BDL AAML 27 6 0 19 Jul 2019
Parameterized quantum circuits as machine learning models Marcello Benedetti Erika Lloyd Stefan H. Sack Mattia Fiorentini 27 869 0 18 Jun 2019
When and Why Metaheuristics Researchers Can Ignore "No Free Lunch" Theorems James McDermott FedML 14 17 0 07 Jun 2019
Provably scale-covariant continuous hierarchical networks based on scale-normalized differential expressions coupled in cascade T. Lindeberg 27 19 0 29 May 2019
AI Feynman: a Physics-Inspired Method for Symbolic Regression S. Udrescu Max Tegmark 22 844 0 27 May 2019
A Selective Overview of Deep Learning Jianqing Fan Cong Ma Yiqiao Zhong BDL VLM 36 136 0 10 Apr 2019
Deep Neural Networks for Rotation-Invariance Approximation and Learning C. Chui Shao-Bo Lin Ding-Xuan Zhou 24 34 0 03 Apr 2019
Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure Been Kim Emily Reif Martin Wattenberg Samy Bengio Michael C. Mozer 33 30 0 04 Mar 2019
How deep is deep enough? -- Quantifying class separability in the hidden layers of deep neural networks Junhong Lin C. Metzner Andreas K. Maier V. Cevher Holger Schulze Patrick Krauss 21 56 0 05 Nov 2018