Non-Gaussian processes and neural networks at finite widths

30 September 2019

Papers citing "Non-Gaussian processes and neural networks at finite widths"

28 / 28 papers shown

Title
Deep Neural Nets as Hamiltonians Mike Winer Boris Hanin 157 0 0 31 Mar 2025
Equivariant Neural Tangent Kernels Philipp Misof Pan Kessel Jan E. Gerken 64 0 0 10 Jun 2024
Bayesian RG Flow in Neural Network Field Theories Jessica N. Howard Marc S. Klinger Anindita Maiti A. G. Stapleton 68 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
A theory of data variability in Neural Network Bayesian inference Javed Lindner David Dahmen Michael Krämer M. Helias BDL 32 1 0 31 Jul 2023
Quantitative CLTs in Deep Neural Networks Stefano Favaro Boris Hanin Domenico Marinucci I. Nourdin G. Peccati BDL 33 11 0 12 Jul 2023
Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks Blake Bordelon Cengiz Pehlevan MLT 38 29 0 06 Apr 2023
Bayes-optimal Learning of Deep Random Networks of Extensive-width Hugo Cui Florent Krzakala Lenka Zdeborová BDL 20 35 0 01 Feb 2023
Catapult Dynamics and Phase Transitions in Quadratic Nets David Meltzer Junyu Liu 27 9 0 18 Jan 2023
A Solvable Model of Neural Scaling Laws A. Maloney Daniel A. Roberts J. Sully 36 51 0 30 Oct 2022
Fast Finite Width Neural Tangent Kernel Roman Novak Jascha Narain Sohl-Dickstein S. Schoenholz AAML 22 53 0 17 Jun 2022
Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling Jiri Hron Roman Novak Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 48 6 0 15 Jun 2022
Gaussian Pre-Activations in Neural Networks: Myth or Reality? Pierre Wolinski Julyan Arbel AI4CE 73 8 0 24 May 2022
Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks Blake Bordelon Cengiz Pehlevan MLT 40 78 0 19 May 2022
Analytic theory for the dynamics of wide quantum neural networks Junyu Liu K. Najafi Kunal Sharma F. Tacchino Liang Jiang Antonio Mezzacapo 27 52 0 30 Mar 2022
Contrasting random and learned features in deep Bayesian linear regression Jacob A. Zavatone-Veth William L. Tong Cengiz Pehlevan BDL MLT 28 26 0 01 Mar 2022
Separation of Scales and a Thermodynamic Description of Feature Learning in Some CNNs Inbar Seroussi Gadi Naveh Zohar Ringel 33 50 0 31 Dec 2021
Rate of Convergence of Polynomial Networks to Gaussian Processes Adam Klukowski 10 14 0 04 Nov 2021
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning Chi-Ken Lu Patrick Shafto BDL 27 4 0 01 Oct 2021
Nonperturbative renormalization for the neural network-QFT correspondence Harold Erbin Vincent Lahoche D. O. Samary 41 30 0 03 Aug 2021
Dataset Distillation with Infinitely Wide Convolutional Networks Timothy Nguyen Roman Novak Lechao Xiao Jaehoon Lee DD 51 229 0 27 Jul 2021
Random Neural Networks in the Infinite Width Limit as Gaussian Processes Boris Hanin BDL 32 43 0 04 Jul 2021
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs Gadi Naveh Zohar Ringel SSL MLT 36 31 0 08 Jun 2021
Explaining Neural Scaling Laws Yasaman Bahri Ethan Dyer Jared Kaplan Jaehoon Lee Utkarsh Sharma 27 250 0 12 Feb 2021
Memorizing without overfitting: Bias, variance, and interpolation in over-parameterized models J. Rocks Pankaj Mehta 18 41 0 26 Oct 2020
Mixed Moments for the Product of Ginibre Matrices Nick Halmagyi Shailesh Lal 14 2 0 20 Jul 2020
Predicting the outputs of finite deep neural networks trained with noisy gradients Gadi Naveh Oded Ben-David H. Sompolinsky Zohar Ringel 19 20 0 02 Apr 2020
Quantifying the probable approximation error of probabilistic inference programs Marco F. Cusumano-Towner Vikash K. Mansinghka 33 5 0 31 May 2016