Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …

The recent striking success of deep neural networks in machine learning raises profound
questions about the theoretical principles underlying their success. For example, what can …

Widely observed neural scaling laws, in which error falls off as a power of the training set
size, model size, or both, have driven substantial performance improvements in deep …

We perform an analysis of the average generalization dynamics of large neural networks
trained using gradient descent. We study the practically-relevant “high-dimensional” regime …

We derive analytical expressions for the generalization performance of kernel regression as
a function of the number of training samples using theoretical methods from Gaussian …

We study generalised linear regression and classification for a synthetically generated
dataset encompassing different problems of interest, such as learning with random features …

Deep neural networks can achieve remarkable generalization performances while
interpolating the training data. Rather than the U-curve emblematic of the bias-variance …

Technological advances have dramatically expanded our ability to probe multi-neuronal
dynamics and connectivity in the brain. However, our ability to extract a simple conceptual …

A central goal of systems neuroscience is to relate an organism's neural activity to behavior.
Neural population analyses often reduce the data dimensionality to focus on relevant activity …

The groundbreaking success of deep learning in many real-world tasks has triggered an
intense effort to theoretically understand the power and limitations of deep learning in the …