Random Neural Networks in the Infinite Width Limit as Gaussian Processes

4 July 2021

Papers citing "Random Neural Networks in the Infinite Width Limit as Gaussian Processes"

26 / 26 papers shown

Title
Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime Francesco Camilli D. Tieplova Eleonora Bergamin Jean Barbier 71 0 0 06 May 2025
Fractal and Regular Geometry of Deep Neural Networks Simmaco Di Lillo Domenico Marinucci Michele Salvi S. Vigogna MDE AI4CE 29 0 0 08 Apr 2025
Deep Neural Nets as Hamiltonians Mike Winer Boris Hanin 73 0 0 31 Mar 2025
Neural Tangent Kernel of Neural Networks with Loss Informed by Differential Operators Weiye Gan Yicheng Li Q. Lin Zuoqiang Shi 34 0 0 14 Mar 2025
Effective Non-Random Extreme Learning Machine Daniela De Canditiis Fabiano Veglianti 66 0 0 25 Nov 2024
Proportional infinite-width infinite-depth limit for deep linear neural networks Federico Bassetti Lucia Ladelli P. Rotondo 62 1 0 22 Nov 2024
On the Impacts of the Random Initialization in the Neural Tangent Kernel Theory Guhan Chen Yicheng Li Qian Lin AAML 30 1 0 08 Oct 2024
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection Steven Adams A. Patané Morteza Lahijanian Luca Laurenti BDL 25 1 0 26 Jul 2024
Wide stable neural networks: Sample regularity, functional convergence and Bayesian inverse problems Tomás Soto 22 0 0 04 Jul 2024
Large Deviations of Gaussian Neural Networks with ReLU activation Quirin Vogel 19 1 0 27 May 2024
Bayesian RG Flow in Neural Network Field Theories Jessica N. Howard Marc S. Klinger Anindita Maiti A. G. Stapleton 60 1 0 27 May 2024
Random ReLU Neural Networks as Non-Gaussian Processes Rahul Parhi Pakshal Bohra Ayoub El Biari Mehrsa Pourya Michael Unser 63 1 0 16 May 2024
Spectral complexity of deep neural networks Simmaco Di Lillo Domenico Marinucci Michele Salvi S. Vigogna BDL 66 1 0 15 May 2024
Neural reproducing kernel Banach spaces and representer theorems for deep networks Francesca Bartolucci E. De Vito Lorenzo Rosasco S. Vigogna 27 4 0 13 Mar 2024
Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes Dario Trevisan UQCV BDL 17 7 0 18 Dec 2023
Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes S. Lehmler Muhammad Saif-ur-Rehman Tobias Glasmachers Ioannis Iossifidis 9 0 0 01 Aug 2023
Quantitative CLTs in Deep Neural Networks Stefano Favaro Boris Hanin Domenico Marinucci I. Nourdin G. Peccati BDL 23 11 0 12 Jul 2023
A Quantitative Functional Central Limit Theorem for Shallow Neural Networks Valentina Cammarota Domenico Marinucci M. Salvi S. Vigogna 10 7 0 29 Jun 2023
Gaussian random field approximation via Stein's method with applications to wide random neural networks Krishnakumar Balasubramanian L. Goldstein Nathan Ross Adil Salim 12 8 0 28 Jun 2023
Structures of Neural Network Effective Theories cCaugin Ararat Tianji Cai Cem Tekin Zhengkang Zhang 47 7 0 03 May 2023
Convergence of neural networks to Gaussian mixture distribution Yasuhiko Asao Ryotaro Sakamoto S. Takagi BDL 14 2 0 26 Apr 2022
Quantitative Gaussian Approximation of Randomly Initialized Deep Neural Networks Andrea Basteri Dario Trevisan BDL 6 21 0 14 Mar 2022
Critical Initialization of Wide and Deep Neural Networks through Partial Jacobians: General Theory and Applications Darshil Doshi Tianyu He Andrey Gromov 20 8 0 23 Nov 2021
Depth induces scale-averaging in overparameterized linear Bayesian neural networks Jacob A. Zavatone-Veth C. Pehlevan BDL UQCV MDE 26 8 0 23 Nov 2021
Rate of Convergence of Polynomial Networks to Gaussian Processes Adam Klukowski 8 14 0 04 Nov 2021
Non-asymptotic approximations of neural networks by Gaussian processes Ronen Eldan Dan Mikulincer T. Schramm 28 24 0 17 Feb 2021