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
Neural networks with linear threshold activations: structure and algorithms Sammy Khalife Hongyu Cheng A. Basu 42 14 0 15 Nov 2021
Theoretical Exploration of Flexible Transmitter Model Jin-Hui Wu Shao-Qun Zhang Yuan Jiang Zhiping Zhou 44 3 0 11 Nov 2021
Scaffolding Sets M. Burhanpurkar Zhun Deng Cynthia Dwork Linjun Zhang 41 9 0 04 Nov 2021
Fourier Neural Networks for Function Approximation R. Subhash K. Yaswanth 28 1 0 21 Oct 2021
Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem Clayton Sanford Vaggos Chatziafratis 16 1 0 19 Oct 2021
Data-driven approaches for predicting spread of infectious diseases through DINNs: Disease Informed Neural Networks Sagi Shaier M. Raissi P. Seshaiyer PINN AI4CE 21 25 0 11 Oct 2021
On the Optimal Memorization Power of ReLU Neural Networks Gal Vardi Gilad Yehudai Ohad Shamir 24 31 0 07 Oct 2021
Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks Annan Yu Chloe Becquey Diana Halikias Matthew Esmaili Mallory Alex Townsend 59 8 0 23 Sep 2021
Dive into Layers: Neural Network Capacity Bounding using Algebraic Geometry Ji Yang Lu Sang Daniel Cremers 14 1 0 03 Sep 2021
Adaptive Group Lasso Neural Network Models for Functions of Few Variables and Time-Dependent Data L. Ho Nicholas Richardson Giang Tran 20 3 0 24 Aug 2021
The staircase property: How hierarchical structure can guide deep learning Emmanuel Abbe Enric Boix-Adserà Matthew Brennan Guy Bresler Dheeraj M. Nagaraj 17 48 0 24 Aug 2021
Towards Understanding Theoretical Advantages of Complex-Reaction Networks Shao-Qun Zhang Gaoxin Wei Zhi-Hua Zhou 23 17 0 15 Aug 2021
Neural Network Approximation of Refinable Functions Ingrid Daubechies Ronald A. DeVore Nadav Dym Shira Faigenbaum-Golovin S. Kovalsky Kung-Chin Lin Josiah Park G. Petrova B. Sober 46 14 0 28 Jul 2021
Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers Colin Wei Yining Chen Tengyu Ma 21 88 0 28 Jul 2021
High-Dimensional Distribution Generation Through Deep Neural Networks Dmytro Perekrestenko Léandre Eberhard Helmut Bölcskei OOD 38 6 0 26 Jul 2021
Estimation of a regression function on a manifold by fully connected deep neural networks Michael Kohler S. Langer U. Reif 22 4 0 20 Jul 2021
Privacy Vulnerability of Split Computing to Data-Free Model Inversion Attacks Xin Dong Hongxu Yin J. Álvarez Jan Kautz Pavlo Molchanov H. T. Kung MIACV 32 8 0 13 Jul 2021
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning Pan Zhou Caiming Xiong Xiaotong Yuan Guosheng Lin SSL 22 12 0 28 Jun 2021
Bayesian Deep Learning Hyperparameter Search for Robust Function Mapping to Polynomials with Noise Nidhin Harilal Udit Bhatia A. Ganguly OOD 22 0 0 23 Jun 2021
Neural Optimization Kernel: Towards Robust Deep Learning Yueming Lyu Ivor Tsang 22 1 0 11 Jun 2021
Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics Carles Domingo-Enrich Youssef Mroueh 27 1 0 10 Jun 2021
Towards Lower Bounds on the Depth of ReLU Neural Networks Christoph Hertrich A. Basu M. D. Summa M. Skutella 34 42 0 31 May 2021
MAGI-X: Manifold-Constrained Gaussian Process Inference for Unknown System Dynamics Chaofan Huang Simin Ma Shihao Yang 27 0 0 27 May 2021
Livewired Neural Networks: Making Neurons That Fire Together Wire Together Thomas Schumacher 30 4 0 17 May 2021
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects Oliver T. Unke Stefan Chmiela M. Gastegger Kristof T. Schütt H. E. Sauceda K. Müller 177 247 0 01 May 2021
Sharp bounds for the number of regions of maxout networks and vertices of Minkowski sums Guido Montúfar Yue Ren Leon Zhang 20 39 0 16 Apr 2021
Deep Nonparametric Regression on Approximate Manifolds: Non-Asymptotic Error Bounds with Polynomial Prefactors Yuling Jiao Guohao Shen Yuanyuan Lin Jian Huang 36 50 0 14 Apr 2021
CovNet: Covariance Networks for Functional Data on Multidimensional Domains Soham Sarkar V. Panaretos 22 6 0 11 Apr 2021
XY Neural Networks N. Stroev N. Berloff 20 0 0 31 Mar 2021
PAC-learning gains of Turing machines over circuits and neural networks Brieuc Pinon Raphaël Jungers Jean-Charles Delvenne AI4CE 21 1 0 23 Mar 2021
The Low-Rank Simplicity Bias in Deep Networks Minyoung Huh H. Mobahi Richard Y. Zhang Brian Cheung Pulkit Agrawal Phillip Isola 27 109 0 18 Mar 2021
Deep ReLU Networks Preserve Expected Length Boris Hanin Ryan Jeong David Rolnick 29 14 0 21 Feb 2021
Understanding algorithmic collusion with experience replay Bing Han 29 2 0 18 Feb 2021
ReLU Neural Networks of Polynomial Size for Exact Maximum Flow Computation Christoph Hertrich Leon Sering 37 10 0 12 Feb 2021
Min-Max-Plus Neural Networks Ye Luo Shi Fan 29 3 0 12 Feb 2021
From Sampling to Optimization on Discrete Domains with Applications to Determinant Maximization Nima Anari T. Vuong 24 9 0 10 Feb 2021
On the Approximation Power of Two-Layer Networks of Random ReLUs Daniel J. Hsu Clayton Sanford Rocco A. Servedio Emmanouil-Vasileios Vlatakis-Gkaragkounis 8 25 0 03 Feb 2021
Depth separation beyond radial functions Luca Venturi Samy Jelassi Tristan Ozuch Joan Bruna 17 15 0 02 Feb 2021
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks Eran Malach Gilad Yehudai Shai Shalev-Shwartz Ohad Shamir 21 20 0 31 Jan 2021
Size and Depth Separation in Approximating Benign Functions with Neural Networks Gal Vardi Daniel Reichman T. Pitassi Ohad Shamir 28 7 0 30 Jan 2021
A simple geometric proof for the benefit of depth in ReLU networks Asaf Amrami Yoav Goldberg 30 1 0 18 Jan 2021
A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks Asaf Noy Yi Tian Xu Y. Aflalo Lihi Zelnik-Manor Rong Jin 39 3 0 12 Jan 2021
A Survey on Neural Network Interpretability Yu Zhang Peter Tiño A. Leonardis K. Tang FaML XAI 144 663 0 28 Dec 2020
Frequency-compensated PINNs for Fluid-dynamic Design Problems Tongtao Zhang Biswadip Dey P. Kakkar A. Dasgupta Amit Chakraborty PINN AI4CE 24 8 0 03 Nov 2020
On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent data Michael Kohler A. Krzyżak 8 12 0 31 Oct 2020
Provable Memorization via Deep Neural Networks using Sub-linear Parameters Sejun Park Jaeho Lee Chulhee Yun Jinwoo Shin FedML MDE 25 36 0 26 Oct 2020
On the Number of Linear Functions Composing Deep Neural Network: Towards a Refined Definition of Neural Networks Complexity Yuuki Takai Akiyoshi Sannai Matthieu Cordonnier 80 4 0 23 Oct 2020
Towards Reflectivity profile inversion through Artificial Neural Networks J. M. Carmona Loaiza Zamaan Raza 21 11 0 15 Oct 2020
Depth-Width Trade-offs for Neural Networks via Topological Entropy Kaifeng Bu Yaobo Zhang Qingxian Luo 13 8 0 15 Oct 2020
Machine Learning Force Fields Oliver T. Unke Stefan Chmiela H. E. Sauceda M. Gastegger I. Poltavsky Kristof T. Schütt A. Tkatchenko K. Müller AI4CE 34 888 0 14 Oct 2020