Towards quantifying information flows: relative entropy in deep neural networks and the renormalization group

14 July 2021

Papers citing "Towards quantifying information flows: relative entropy in deep neural networks and the renormalization group"

9 / 9 papers shown

Title
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
Wilsonian Renormalization of Neural Network Gaussian Processes Jessica N. Howard Ro Jefferson Anindita Maiti Z. Ringel BDL 64 3 0 09 May 2024
Tensor networks for interpretable and efficient quantum-inspired machine learning Shirli Ran Gang Su 39 7 0 19 Nov 2023
Neural Network Field Theories: Non-Gaussianity, Actions, and Locality M. Demirtaş James Halverson Anindita Maiti M. Schwartz Keegan Stoner AI4CE 23 10 0 06 Jul 2023
Bayesian Renormalization D. Berman Marc S. Klinger A. G. Stapleton 25 15 0 17 May 2023
Criticality versus uniformity in deep neural networks A. Bukva Jurriaan de Gier Kevin T. Grosvenor R. Jefferson K. Schalm Eliot Schwander 21 3 0 10 Apr 2023
The edge of chaos: quantum field theory and deep neural networks Kevin T. Grosvenor R. Jefferson 27 22 0 27 Sep 2021
Entropic alternatives to initialization Daniele Musso 37 1 0 16 Jul 2021
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks Lechao Xiao Yasaman Bahri Jascha Narain Sohl-Dickstein S. Schoenholz Jeffrey Pennington 220 348 0 14 Jun 2018