Deep classifier neural networks enter the terminal phase of training (TPT) when training
error reaches zero and tend to exhibit intriguing Neural Collapse (NC) properties. Neural …

In this paper, we show that although the minimizers of cross-entropy and related
classification losses are off at infinity, network weights learned by gradient flow converge in …

Generalization of deep networks has been of great interest in recent years, resulting in a
number of theoretically and empirically motivated complexity measures. However, most …

To make deliberate progress towards more intelligent and more human-like artificial
systems, we need to be following an appropriate feedback signal: we need to be able to …

We describe the new field of the mathematical analysis of deep learning. This field emerged
around a list of research questions that were not answered within the classical framework of …

Recent work has shown that the accuracy of machine learning models can vary substantially
when evaluated on a distribution that even slightly differs from that of the training data. As a …

Channel pruning is a class of powerful methods for model compression. When pruning a
neural network, it's ideal to obtain a sub-network with higher accuracy. However, a sub …

Recent developments in large-scale machine learning suggest that by scaling up data,
model size and training time properly, one might observe that improvements in pre-training …

This work studies the design of neural networks that can process the weights or gradients of
other neural networks, which we refer to as neural functional networks (NFNs). Despite a …

Existing work on understanding deep learning often employs measures that compress all
data-dependent information into a few numbers. In this work, we adopt a perspective based …