In recent years, several results in the supervised learning setting suggested that classical statistical learning-theoretic measures, such as VC dimension, do not adequately explain the performance of deep learning models which prompted a slew of work in the infinite-width and iteration regimes.

Learning and Deep Graph Neural Networks to learn gene representations from single-cell sequencing and spatial transcriptomic data.

This paper identifies astructural property of data distributions that enables deep neural networks to learn hierarchically.

We study the problem of combining neural networks with symbolic reasoning.

Understanding the neural basis of behavior is a fundamental goal in neuroscience. Current research in large-scale neuro-behavioral data analysis often relies on decoding models, which quantify behavioral information in neural data but lack details on behavior encoding. This raises an intriguing scientific question: " how