In the last few years, machine learning (ML) and artificial intelligence have seen a new wave
of publicity fueled by the huge and ever‐increasing amount of data and computational …

Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

Neural sequence models based on the transformer architecture have demonstrated
remarkable\emph {in-context learning}(ICL) abilities, where they can perform new tasks …

Aerial robots are widely deployed, but highly cluttered environments such as dense forests
remain inaccessible to drones and even more so to swarms of drones. In these scenarios …

Federated learning (FL) is a decentralized and privacy-preserving machine learning
technique in which a group of clients collaborate with a server to learn a global model …

Federated learning is a key scenario in modern large-scale machine learning where the
data remains distributed over a large number of clients and the task is to learn a centralized …

This open-source book represents our attempt to make deep learning approachable,
teaching readers the concepts, the context, and the code. The entire book is drafted in …

The speed of gradient descent for convex Lipschitz functions is highly dependent on the
choice of learning rate. Setting the learning rate to achieve the optimal convergence rate …

A high school student can create deep Q-learning code to control her robot, without any
understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …

We present a novel approach to federated learning that endows its aggregation process with
greater robustness to potential poisoning of local data or model parameters of participating …