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

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

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 …

Universality is a key hypothesis in mechanistic interpretability–that different models learn
similar features and circuits when trained on similar tasks. In this work, we study the …

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 …

The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …

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