The Power of Depth for Feedforward Neural Networks

12 December 2015

Papers citing "The Power of Depth for Feedforward Neural Networks"

50 / 367 papers shown

Title
Approximating smooth functions by deep neural networks with sigmoid activation function S. Langer 24 66 0 08 Oct 2020
Explaining Deep Neural Networks Oana-Maria Camburu XAI FAtt 33 26 0 04 Oct 2020
Deep Equals Shallow for ReLU Networks in Kernel Regimes A. Bietti Francis R. Bach 30 86 0 30 Sep 2020
Grow-Push-Prune: aligning deep discriminants for effective structural network compression Qing Tian Tal Arbel James J. Clark 16 8 0 29 Sep 2020
Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don't E. Weinan Chao Ma Stephan Wojtowytsch Lei Wu AI4CE 22 133 0 22 Sep 2020
Review: Deep Learning in Electron Microscopy Jeffrey M. Ede 34 79 0 17 Sep 2020
Error estimate for a universal function approximator of ReLU network with a local connection Jaeyeon Kang Sunghwan Moon 11 0 0 03 Sep 2020
When Hardness of Approximation Meets Hardness of Learning Eran Malach Shai Shalev-Shwartz 16 9 0 18 Aug 2020
Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network Based Vector-to-Vector Regression Jun Qi Jun Du Sabato Marco Siniscalchi Xiaoli Ma Chin-Hui Lee 30 41 0 04 Aug 2020
Deep frequency principle towards understanding why deeper learning is faster Zhi-Qin John Xu Hanxu Zhou 24 44 0 28 Jul 2020
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems Donsub Rim Luca Venturi Joan Bruna Benjamin Peherstorfer 28 9 0 28 Jul 2020
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion Detection Fares Meghdouri Maximilian Bachl Tanja Zseby 21 3 0 27 Jul 2020
Rewiring the Transformer with Depth-Wise LSTMs Hongfei Xu Yang Song Qiuhui Liu Josef van Genabith Deyi Xiong 42 6 0 13 Jul 2020
Expressivity of Deep Neural Networks Ingo Gühring Mones Raslan Gitta Kutyniok 16 51 0 09 Jul 2020
Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport T. L. J. Ng A. Zammit‐Mangion 6 6 0 01 Jul 2020
Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization Sang Michael Xie Tengyu Ma Percy Liang 35 13 0 29 Jun 2020
Towards Understanding Hierarchical Learning: Benefits of Neural Representations Minshuo Chen Yu Bai J. Lee T. Zhao Huan Wang Caiming Xiong R. Socher SSL 10 48 0 24 Jun 2020
The Depth-to-Width Interplay in Self-Attention Yoav Levine Noam Wies Or Sharir Hofit Bata Amnon Shashua 30 45 0 22 Jun 2020
No one-hidden-layer neural network can represent multivariable functions Masayo Inoue Mana Futamura H. Ninomiya MLT 6 0 0 19 Jun 2020
Image Response Regression via Deep Neural Networks Daiwei Zhang Lexin Li Chandra S. Sripada Jian Kang 14 7 0 17 Jun 2020
Measuring Model Complexity of Neural Networks with Curve Activation Functions X. Hu Weiqing Liu Jiang Bian J. Pei 14 20 0 16 Jun 2020
Minimum Width for Universal Approximation Sejun Park Chulhee Yun Jaeho Lee Jinwoo Shin 33 122 0 16 Jun 2020
Recent Advances in 3D Object and Hand Pose Estimation Vincent Lepetit 3DH 40 14 0 10 Jun 2020
Approximating Lipschitz continuous functions with GroupSort neural networks Ugo Tanielian Maxime Sangnier Gérard Biau 19 36 0 09 Jun 2020
Liquid Time-constant Networks Ramin Hasani Mathias Lechner Alexander Amini Daniela Rus Radu Grosu AI4TS AI4CE 24 215 0 08 Jun 2020
Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth Guy Bresler Dheeraj M. Nagaraj 11 21 0 07 Jun 2020
Neural Networks with Small Weights and Depth-Separation Barriers Gal Vardi Ohad Shamir 8 18 0 31 May 2020
Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size Christoph Hertrich M. Skutella 50 21 0 28 May 2020
Physically interpretable machine learning algorithm on multidimensional non-linear fields Rem-Sophia Mouradi C. Goeury O. Thual F. Zaoui P. Tassi OOD 4 7 0 28 May 2020
Is deeper better? It depends on locality of relevant features Takashi Mori Masahito Ueda OOD 25 4 0 26 May 2020
PDE constraints on smooth hierarchical functions computed by neural networks Khashayar Filom Konrad Paul Kording Roozbeh Farhoodi 19 0 0 18 May 2020
Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space Nikolas Nusken Lorenz Richter AI4CE 19 104 0 11 May 2020
A survey on modern trainable activation functions Andrea Apicella Francesco Donnarumma Francesco Isgrò R. Prevete 36 366 0 02 May 2020
The Information Bottleneck Problem and Its Applications in Machine Learning Ziv Goldfeld Yury Polyanskiy 23 129 0 30 Apr 2020
A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions Yulong Lu Jianfeng Lu 18 19 0 19 Apr 2020
Universal Approximation on the Hypersphere T. L. J. Ng Kwok-Kun Kwong 18 3 0 14 Apr 2020
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization Zhi Han Siquan Yu Shao-Bo Lin Ding-Xuan Zhou OOD 11 38 0 01 Apr 2020
Depth Enables Long-Term Memory for Recurrent Neural Networks A. Ziv 16 0 0 23 Mar 2020
Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisation Arnulf Jentzen Timo Welti 22 15 0 03 Mar 2020
Better Depth-Width Trade-offs for Neural Networks through the lens of Dynamical Systems Vaggos Chatziafratis Sai Ganesh Nagarajan Ioannis Panageas 20 15 0 02 Mar 2020
A closer look at the approximation capabilities of neural networks Kai Fong Ernest Chong 21 16 0 16 Feb 2020
Quasi-Equivalence of Width and Depth of Neural Networks Fenglei Fan Rongjie Lai Ge Wang 22 11 0 06 Feb 2020
A Deep Conditioning Treatment of Neural Networks Naman Agarwal Pranjal Awasthi Satyen Kale AI4CE 25 15 0 04 Feb 2020
DNNs as Layers of Cooperating Classifiers Marelie Hattingh Davel Marthinus W. Theunissen Arnold M. Pretorius E. Barnard 11 7 0 17 Jan 2020
Approximation smooth and sparse functions by deep neural networks without saturation Xia Liu 19 1 0 13 Jan 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 38 300 0 08 Jan 2020
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group) Zied Bouraoui Antoine Cornuéjols Thierry Denoeux Sebastien Destercke Didier Dubois ... Jérôme Mengin H. Prade Steven Schockaert M. Serrurier Christel Vrain 21 13 0 13 Dec 2019
Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem Vaggos Chatziafratis Sai Ganesh Nagarajan Ioannis Panageas Tianlin Li 17 21 0 09 Dec 2019
Stationary Points of Shallow Neural Networks with Quadratic Activation Function D. Gamarnik Eren C. Kizildag Ilias Zadik 19 13 0 03 Dec 2019
Thick-Net: Parallel Network Structure for Sequential Modeling Yu-Xuan Li Jin-Yuan Liu Liang Li Xiang Guan 11 0 0 19 Nov 2019