Picture for Berfin Simsek

Berfin Simsek

Learning Associative Memories with Gradient Descent

Add code
Feb 28, 2024
Viaarxiv icon

The Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escaping, and Network Embedding

Add code
Feb 08, 2024
Viaarxiv icon

Expand-and-Cluster: Exact Parameter Recovery of Neural Networks

Add code
Apr 25, 2023
Viaarxiv icon

Understanding out-of-distribution accuracies through quantifying difficulty of test samples

Add code
Mar 28, 2022
Figure 1 for Understanding out-of-distribution accuracies through quantifying difficulty of test samples
Figure 2 for Understanding out-of-distribution accuracies through quantifying difficulty of test samples
Figure 3 for Understanding out-of-distribution accuracies through quantifying difficulty of test samples
Figure 4 for Understanding out-of-distribution accuracies through quantifying difficulty of test samples
Viaarxiv icon

Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape

Add code
Jul 05, 2019
Figure 1 for Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape
Figure 2 for Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape
Figure 3 for Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape
Figure 4 for Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape
Viaarxiv icon