Benefits of depth in neural networks

14 February 2016

Papers citing "Benefits of depth in neural networks"

50 / 353 papers shown

Title
Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision Arturs Berzins 25 5 0 12 Jun 2023
Representational Strengths and Limitations of Transformers Clayton Sanford Daniel J. Hsu Matus Telgarsky 22 81 0 05 Jun 2023
On the Expressive Power of Neural Networks J. Holstermann 17 3 0 31 May 2023
Probabilistic computation and uncertainty quantification with emerging covariance He Ma Yong Qi Li Zhang Wenlian Lu Jianfeng Feng 11 1 0 30 May 2023
Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation Liang Li Yifei Duan Guanghua Ji Yongqiang Cai MLT 32 13 0 29 May 2023
Data Topology-Dependent Upper Bounds of Neural Network Widths Sangmin Lee Jong Chul Ye 26 0 0 25 May 2023
VanillaNet: the Power of Minimalism in Deep Learning Hanting Chen Yunhe Wang Jianyuan Guo Dacheng Tao VLM 34 85 0 22 May 2023
Minimax optimal density estimation using a shallow generative model with a one-dimensional latent variable Hyeok Kyu Kwon Minwoo Chae DRL 23 3 0 11 May 2023
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks Eshaan Nichani Alexandru Damian Jason D. Lee MLT 38 13 0 11 May 2023
Approximation of Nonlinear Functionals Using Deep ReLU Networks Linhao Song Jun Fan Dirong Chen Ding-Xuan Zhou 15 14 0 10 Apr 2023
Depth Separation with Multilayer Mean-Field Networks Y. Ren Mo Zhou Rong Ge OOD 14 3 0 03 Apr 2023
Multi-task neural networks by learned contextual inputs Anders T. Sandnes B. Grimstad O. Kolbjørnsen 14 1 0 01 Mar 2023
Are More Layers Beneficial to Graph Transformers? Haiteng Zhao Shuming Ma Dongdong Zhang Zhi-Hong Deng Furu Wei 27 12 0 01 Mar 2023
Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes Christian Haase Christoph Hertrich Georg Loho 31 21 0 24 Feb 2023
Sharp Lower Bounds on Interpolation by Deep ReLU Neural Networks at Irregularly Spaced Data Jonathan W. Siegel 6 2 0 02 Feb 2023
On the Lipschitz Constant of Deep Networks and Double Descent Matteo Gamba Hossein Azizpour Marten Bjorkman 25 7 0 28 Jan 2023
Deep Convolutional Framelet Denoising for Panoramic by Mixed Wavelet Integration Masoud Mohammadi Seyed Javad Mahdavi Chabok MedIm 14 0 0 25 Jan 2023
Expected Gradients of Maxout Networks and Consequences to Parameter Initialization Hanna Tseran Guido Montúfar ODL 22 0 0 17 Jan 2023
Limitations on approximation by deep and shallow neural networks G. Petrova P. Wojtaszczyk 11 7 0 30 Nov 2022
A Kernel Perspective of Skip Connections in Convolutional Networks Daniel Barzilai Amnon Geifman Meirav Galun Ronen Basri 17 11 0 27 Nov 2022
Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces Jonathan W. Siegel 20 28 0 25 Nov 2022
LU decomposition and Toeplitz decomposition of a neural network Yucong Liu Simiao Jiao Lek-Heng Lim 30 7 0 25 Nov 2022
Leveraging Heteroscedastic Uncertainty in Learning Complex Spectral Mapping for Single-channel Speech Enhancement Kuan-Lin Chen Daniel D. E. Wong Ke Tan Buye Xu Anurag Kumar V. Ithapu 19 1 0 16 Nov 2022
Universal Time-Uniform Trajectory Approximation for Random Dynamical Systems with Recurrent Neural Networks A. Bishop 37 1 0 15 Nov 2022
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks Anders Aamand Justin Y. Chen Piotr Indyk Shyam Narayanan R. Rubinfeld Nicholas Schiefer Sandeep Silwal Tal Wagner 39 21 0 06 Nov 2022
When Expressivity Meets Trainability: Fewer than $n$ Neurons Can Work Jiawei Zhang Yushun Zhang Mingyi Hong Ruoyu Sun Z. Luo 26 10 0 21 Oct 2022
Transformers Learn Shortcuts to Automata Bingbin Liu Jordan T. Ash Surbhi Goel A. Krishnamurthy Cyril Zhang OffRL LRM 40 155 0 19 Oct 2022
Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions Kuan-Lin Chen H. Garudadri Bhaskar D. Rao 11 18 0 13 Oct 2022
On Scrambling Phenomena for Randomly Initialized Recurrent Networks Vaggos Chatziafratis Ioannis Panageas Clayton Sanford S. Stavroulakis 11 2 0 11 Oct 2022
Factor Augmented Sparse Throughput Deep ReLU Neural Networks for High Dimensional Regression Jianqing Fan Yihong Gu 14 21 0 05 Oct 2022
Enumeration of max-pooling responses with generalized permutohedra Laura Escobar Patricio Gallardo Javier González-Anaya J. L. González Guido Montúfar A. Morales 14 1 0 29 Sep 2022
Achieve the Minimum Width of Neural Networks for Universal Approximation Yongqiang Cai 9 18 0 23 Sep 2022
Optimal bump functions for shallow ReLU networks: Weight decay, depth separation and the curse of dimensionality Stephan Wojtowytsch 22 1 0 02 Sep 2022
Blessing of Nonconvexity in Deep Linear Models: Depth Flattens the Optimization Landscape Around the True Solution Jianhao Ma S. Fattahi 42 5 0 15 Jul 2022
Concentration inequalities and optimal number of layers for stochastic deep neural networks Michele Caprio Sayan Mukherjee BDL 17 1 0 22 Jun 2022
Deep Partial Least Squares for Empirical Asset Pricing M. Dixon Nicholas G. Polson Kemen Goicoechea 26 2 0 20 Jun 2022
Coin Flipping Neural Networks Yuval Sieradzki Nitzan Hodos Gal Yehuda Assaf Schuster UQCV 27 3 0 18 Jun 2022
$Intrinsic dimensionality and generalization properties of the $\mathcal{R}$-norm inductive bias$ Intrinsic dimensionality and generalization properties of the $\mathcal{R}$ -norm inductive bias Navid Ardeshir Daniel J. Hsu Clayton Sanford CML AI4CE 18 6 0 10 Jun 2022
A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks E. M. Achour Armand Foucault Sébastien Gerchinovitz Franccois Malgouyres 29 7 0 09 Jun 2022
Exponential Separations in Symmetric Neural Networks Aaron Zweig Joan Bruna 27 7 0 02 Jun 2022
Asymptotic Properties for Bayesian Neural Network in Besov Space Kyeongwon Lee Jaeyong Lee BDL 11 4 0 01 Jun 2022
Universality of Group Convolutional Neural Networks Based on Ridgelet Analysis on Groups Sho Sonoda Isao Ishikawa Masahiro Ikeda 30 9 0 30 May 2022
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power Binghui Li Jikai Jin Han Zhong J. Hopcroft Liwei Wang OOD 79 27 0 27 May 2022
Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks Zhiwei Bai Tao Luo Z. Xu Yaoyu Zhang 23 4 0 26 May 2022
CNNs Avoid Curse of Dimensionality by Learning on Patches Vamshi C. Madala S. Chandrasekaran Jason Bunk UQCV 27 5 0 22 May 2022
On the inability of Gaussian process regression to optimally learn compositional functions M. Giordano Kolyan Ray Johannes Schmidt-Hieber 33 12 0 16 May 2022
ExSpliNet: An interpretable and expressive spline-based neural network Daniele Fakhoury Emanuele Fakhoury H. Speleers 11 33 0 03 May 2022
$Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete$ Training Fully Connected Neural Networks is $\exists\mathbb{R}$ -Complete Daniel Bertschinger Christoph Hertrich Paul Jungeblut Tillmann Miltzow Simon Weber OffRL 57 30 0 04 Apr 2022
How do noise tails impact on deep ReLU networks? Jianqing Fan Yihong Gu Wen-Xin Zhou ODL 38 13 0 20 Mar 2022
Towards understanding deep learning with the natural clustering prior Simon Carbonnelle 13 0 0 15 Mar 2022