Who is Afraid of Big Bad Minima? Analysis of Gradient-Flow in a Spiked
Matrix-Tensor Model

Who is Afraid of Big Bad Minima? Analysis of Gradient-Flow in a Spiked Matrix-Tensor Model

18 July 2019

Stefano Sarao Mannelli

Florent Krzakala

Lenka Zdeborová

Papers citing "Who is Afraid of Big Bad Minima? Analysis of Gradient-Flow in a Spiked Matrix-Tensor Model"

10 / 10 papers shown

Title
Sudakov-Fernique post-AMP, and a new proof of the local convexity of the TAP free energy Michael Celentano 46 20 0 19 Aug 2022
High-dimensional limit theorems for SGD: Effective dynamics and critical scaling Gerard Ben Arous Reza Gheissari Aukosh Jagannath 62 58 0 08 Jun 2022
Universal characteristics of deep neural network loss surfaces from random matrix theory Nicholas P. Baskerville J. Keating F. Mezzadri J. Najnudel Diego Granziol 32 4 0 17 May 2022
Optimal learning rate schedules in high-dimensional non-convex optimization problems Stéphane dÁscoli Maria Refinetti Giulio Biroli 23 7 0 09 Feb 2022
Selective Multiple Power Iteration: from Tensor PCA to gradient-based exploration of landscapes M. Ouerfelli M. Tamaazousti V. Rivasseau 39 7 0 23 Dec 2021
An Analytical Theory of Curriculum Learning in Teacher-Student Networks Luca Saglietti Stefano Sarao Mannelli Andrew M. Saxe 27 25 0 15 Jun 2021
Appearance of Random Matrix Theory in Deep Learning Nicholas P. Baskerville Diego Granziol J. Keating 15 11 0 12 Feb 2021
A spin-glass model for the loss surfaces of generative adversarial networks Nicholas P. Baskerville J. Keating F. Mezzadri J. Najnudel GAN 30 12 0 07 Jan 2021
Quantitative Propagation of Chaos for SGD in Wide Neural Networks Valentin De Bortoli Alain Durmus Xavier Fontaine Umut Simsekli 32 25 0 13 Jul 2020
Thresholds of descending algorithms in inference problems Stefano Sarao Mannelli Lenka Zdeborova AI4CE 24 4 0 02 Jan 2020