Representation Benefits of Deep Feedforward Networks

27 September 2015

Papers citing "Representation Benefits of Deep Feedforward Networks"

50 / 63 papers shown

Title
Non-identifiability distinguishes Neural Networks among Parametric Models Sourav Chatterjee Timothy Sudijono 35 0 0 25 Apr 2025
On Space Folds of ReLU Neural Networks Michal Lewandowski Hamid Eghbalzadeh Bernhard Heinzl Raphael Pisoni Bernhard A.Moser MLT 87 1 0 17 Feb 2025
Extracting Formulae in Many-Valued Logic from Deep Neural Networks Yani Zhang Helmut Bölcskei 24 0 0 22 Jan 2024
Expressivity and Approximation Properties of Deep Neural Networks with ReLU $^k$ Activation Juncai He Tong Mao Jinchao Xu 45 3 0 27 Dec 2023
Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision Arturs Berzins 27 5 0 12 Jun 2023
The Tunnel Effect: Building Data Representations in Deep Neural Networks Wojciech Masarczyk M. Ostaszewski Ehsan Imani Razvan Pascanu Piotr Milo's Tomasz Trzciñski 41 19 0 31 May 2023
Embeddings between Barron spaces with higher order activation functions T. J. Heeringa L. Spek Felix L. Schwenninger C. Brune 42 3 0 25 May 2023
Multi-Path Transformer is Better: A Case Study on Neural Machine Translation Ye Lin Shuhan Zhou Yanyang Li Anxiang Ma Tong Xiao Jingbo Zhu 38 0 0 10 May 2023
When Deep Learning Meets Polyhedral Theory: A Survey Joey Huchette Gonzalo Muñoz Thiago Serra Calvin Tsay AI4CE 96 33 0 29 Apr 2023
The R-mAtrIx Net Shailesh Lal Suvajit Majumder E. Sobko 24 5 0 14 Apr 2023
Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes Christian Haase Christoph Hertrich Georg Loho 34 22 0 24 Feb 2023
Optimal Approximation Complexity of High-Dimensional Functions with Neural Networks Vincent P. H. Goverse Jad Hamdan Jared Tanner 31 0 0 30 Jan 2023
Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions Junyang Cai Khai-Nguyen Nguyen Nishant Shrestha Aidan Good Ruisen Tu Xin Yu Shandian Zhe Thiago Serra MLT 40 7 0 19 Jan 2023
Expected Gradients of Maxout Networks and Consequences to Parameter Initialization Hanna Tseran Guido Montúfar ODL 32 0 0 17 Jan 2023
Effects of Data Geometry in Early Deep Learning Saket Tiwari George Konidaris 82 7 0 29 Dec 2022
Towards Global Neural Network Abstractions with Locally-Exact Reconstruction Edoardo Manino I. Bessa Lucas C. Cordeiro 21 1 0 21 Oct 2022
Curved Representation Space of Vision Transformers Juyeop Kim Junha Park Songkuk Kim Jongseok Lee ViT 41 6 0 11 Oct 2022
Limitations of neural network training due to numerical instability of backpropagation Clemens Karner V. Kazeev P. Petersen 40 3 0 03 Oct 2022
Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL Fengzhuo Zhang Boyi Liu Kaixin Wang Vincent Y. F. Tan Zhuoran Yang Zhaoran Wang OffRL LRM 51 10 0 20 Sep 2022
Universal Solutions of Feedforward ReLU Networks for Interpolations Changcun Huang 25 2 0 16 Aug 2022
Blessing of Nonconvexity in Deep Linear Models: Depth Flattens the Optimization Landscape Around the True Solution Jianhao Ma S. Fattahi 46 5 0 15 Jul 2022
Lower and Upper Bounds for Numbers of Linear Regions of Graph Convolutional Networks Hao Chen Yu Wang Huan Xiong GNN 21 6 0 01 Jun 2022
CNNs Avoid Curse of Dimensionality by Learning on Patches Vamshi C. Madala S. Chandrasekaran Jason Bunk UQCV 33 5 0 22 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 64 30 0 04 Apr 2022
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks Xin Yu Thiago Serra Srikumar Ramalingam Shandian Zhe 44 48 0 09 Mar 2022
Selective Network Linearization for Efficient Private Inference Minsu Cho Ameya Joshi S. Garg Brandon Reagen C. Hegde 19 43 0 04 Feb 2022
Training Thinner and Deeper Neural Networks: Jumpstart Regularization Carles Roger Riera Molina Camilo Rey Thiago Serra Eloi Puertas O. Pujol 27 4 0 30 Jan 2022
Expressivity of Neural Networks via Chaotic Itineraries beyond Sharkovsky's Theorem Clayton Sanford Vaggos Chatziafratis 16 1 0 19 Oct 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
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs) Michael Penwarden Shandian Zhe A. Narayan Robert M. Kirby 34 43 0 25 Jun 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 ReLU Networks Preserve Expected Length Boris Hanin Ryan Jeong David Rolnick 29 14 0 21 Feb 2021
A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks Asaf Noy Yi Tian Xu Y. Aflalo Lihi Zelnik-Manor Rong Jin 41 3 0 12 Jan 2021
Hierarchically Compositional Tasks and Deep Convolutional Networks Arturo Deza Q. Liao Andrzej Banburski T. Poggio BDL OOD 33 2 0 24 Jun 2020
Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size Christoph Hertrich M. Skutella 52 21 0 28 May 2020
Neural Contextual Bandits with UCB-based Exploration Dongruo Zhou Lihong Li Quanquan Gu 36 15 0 11 Nov 2019
Optimal Function Approximation with Relu Neural Networks Bo Liu Yi Liang 25 33 0 09 Sep 2019
Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Zhaoqiang Liu Jonathan Scarlett 19 38 0 28 Aug 2019
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization T. Poggio Andrzej Banburski Q. Liao ODL 40 161 0 25 Aug 2019
A Review on Deep Learning in Medical Image Reconstruction Hai-Miao Zhang Bin Dong MedIm 35 122 0 23 Jun 2019
Nonlinear Approximation and (Deep) ReLU Networks Ingrid Daubechies Ronald A. DeVore S. Foucart Boris Hanin G. Petrova 22 138 0 05 May 2019
Is Deeper Better only when Shallow is Good? Eran Malach Shai Shalev-Shwartz 28 45 0 08 Mar 2019
Deep Neural Network Approximation Theory Dennis Elbrächter Dmytro Perekrestenko Philipp Grohs Helmut Bölcskei 19 207 0 08 Jan 2019
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou Yuan Cao Dongruo Zhou Quanquan Gu ODL 33 446 0 21 Nov 2018
Statistical Characteristics of Deep Representations: An Empirical Investigation Daeyoung Choi Kyungeun Lee Changho Shin Stephen J. Roberts AI4TS 21 2 0 08 Nov 2018
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity Chulhee Yun S. Sra Ali Jadbabaie 28 117 0 17 Oct 2018
The universal approximation power of finite-width deep ReLU networks Dmytro Perekrestenko Philipp Grohs Dennis Elbrächter Helmut Bölcskei 21 36 0 05 Jun 2018
Deep Learning Works in Practice. But Does it Work in Theory? L. Hoang R. Guerraoui PINN 44 3 0 31 Jan 2018
The exploding gradient problem demystified - definition, prevalence, impact, origin, tradeoffs, and solutions George Philipp D. Song J. Carbonell ODL 35 46 0 15 Dec 2017
Approximating Continuous Functions by ReLU Nets of Minimal Width Boris Hanin Mark Sellke 50 229 0 31 Oct 2017