Advances in artificial intelligence are stimulating interest in neuroscience. However, most
attention is given to discrete tasks with simple action spaces, such as board games and …

Early research in the field of intuitive physics provided extensive evidence that humans
succumb to common misconceptions and biases when predicting, judging, and explaining …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

This paper introduces a new model to learn graph neural networks equivariant to rotations,
translations, reflections and permutations called E (n)-Equivariant Graph Neural Networks …

Here we present a machine learning framework and model implementation that can learn to
simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and …

Even though neural networks enjoy widespread use, they still struggle to learn the basic
laws of physics. How might we endow them with better inductive biases? In this paper, we …

Understanding and interacting with everyday physical scenes requires rich knowledge
about the structure of the world, represented either implicitly in a value or policy function, or …

Abstract Reasoning about objects, relations, and physics is central to human intelligence,
and a key goal of artificial intelligence. Here we introduce the interaction network, a model …

Numerical optimization has played a central role in addressing key signal processing (SP)
problems. Highly effective methods have been developed for a large variety of SP …

Learning physics-based locomotion skills is a difficult problem, leading to solutions that
typically exploit prior knowledge of various forms. In this paper we aim to learn a variety of …