2 citations · 4 across the 4 of their papers we have counts for
4 papers
Stochastic Gradient Descent in the Saddle-to-Saddle Regime of Deep Linear Networks
Guillaume Corlouer, Avi Semler, Alexander Strang +1
Deep linear networks (DLNs) are used as an analytically tractable model of the training dynamics of deep neural networks. While gradient descent in DLNs is known to exhibit saddle-…
From Density Matrices to Phase Transitions in Deep Learning: Spectral Early Warnings and Interpretability
Max Hennick, Guillaume Corlouer
A key problem in the modern study of AI is predicting and understanding emergent capabilities in models during training. Inspired by methods for studying reactions in quantum chemi…
Structured World Representations in Maze-Solving Transformers
Michael Igorevich Ivanitskiy, Alex F. Spies, Tilman Räuker +9
Transformer models underpin many recent advances in practical machine learning applications, yet understanding their internal behavior continues to elude researchers. Given the siz…
A Configurable Library for Generating and Manipulating Maze Datasets
Michael Igorevich Ivanitskiy, Rusheb Shah, Alex F. Spies +8
Understanding how machine learning models respond to distributional shifts is a key research challenge. Mazes serve as an excellent testbed due to varied generation algorithms offe…