Highlights•Artificial and biological neural networks can be analyzed using similar
methods.•Neural analysis has revealed similarities between the representations in artificial …

Similarity and categorization are fundamental processes in human cognition that help
complex organisms make sense of the cacophony of information in their environment. These …

Despite their wide adoption, the underlying training and memorization dynamics of very
large language models is not well understood. We empirically study exact memorization in …

Convolutional neural networks (CNNs) have so far been the de-facto model for visual data.
Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or …

Recently proposed self-supervised learning approaches have been successful for pre-
training speech representation models. The utility of these learned representations has been …

Neural networks often learn to make predictions that overly rely on spurious corre-lation
existing in the dataset, which causes the model to be biased. While previous work tackles …

An important research direction in machine learning has centered around developing meta-
learning algorithms to tackle few-shot learning. An especially successful algorithm has been …

Recent work has sought to understand the behavior of neural networks by comparing
representations between layers and between different trained models. We examine methods …

Transfer learning from natural image datasets, particularly ImageNet, using standard large
models and corresponding pretrained weights has become a de-facto method for deep …

A key factor in the success of deep neural networks is the ability to scale models to improve
performance by varying the architecture depth and width. This simple property of neural …