Deep learning models are widely being employed for object detection due to their high
performance. However, the majority of applications that require object detection are …

Algorithmic reasoning requires capabilities which are most naturally understood through
recurrent models of computation, like the Turing machine. However, Transformer models …

Neural networks have achieved remarkable empirical performance, while the current
theoretical analysis is not adequate for understanding their success, eg, the Neural Tangent …

One of the central questions in the theory of deep learning is to understand how neural
networks learn hierarchical features. The ability of deep networks to extract salient features …

This paper considers the following question: how well can depth-two ReLU networks with
randomly initialized bottom-level weights represent smooth functions? We give near …

Abstract Machine learning algorithms relying on deep neural networks recently allowed a
great leap forward in artificial intelligence. Despite the popularity of their applications, the …

Depth separation results propose a possible theoretical explanation for the benefits of deep
neural networks over shallower architectures, establishing that the former possess superior …

In the present review, the merits and demerits of machine learning (ML) in materials science
are discussed, compared with first principles calculations (PDE (partial differential …

We solve an open question from Lu et al.(2017), by showing that any target network with
inputs in $\mathbb {R}^ d $ can be approximated by a width $ O (d) $ network (independent …

We study the memorization power of feedforward ReLU neural networks. We show that such
networks can memorize any $ N $ points that satisfy a mild separability assumption using …