The popularity of machine learning (ML), deep learning (DL) and artificial intelligence (AI)
has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML …

Deep learning has triggered the current rise of artificial intelligence and is the workhorse of
today's machine intelligence. Numerous success stories have rapidly spread all over …

Transformers are emerging as the new workhorse of NLP, showing great success across
tasks. Unlike LSTMs, transformers process input sequences entirely through self-attention …

Our understanding of the generalization capabilities of neural networks NNs is still
incomplete. Prevailing explanations are based on implicit biases of gradient descent GD but …

The success of deep networks has been attributed in part to their expressivity: per
parameter, deep networks can approximate a richer class of functions than shallow …

Despite the widespread success of Transformers on NLP tasks, recent works have found
that they struggle to model several formal languages when compared to recurrent models …

In this paper, we study the statistical limits of deep learning techniques for solving elliptic
partial differential equations (PDEs) from random samples using the Deep Ritz Method …

Deep neural networks (DNNs) often rely on easy-to-learn discriminatory features, or cues,
that are not necessarily essential to the problem at hand. For example, ducks in an image …

Learning the underlying patterns in data goes beyond instance-based generalization to
external knowledge represented in structured graphs or networks. Deep learning that …

Adversarial examples for neural network image classifiers are known to be transferable:
examples optimized to be misclassified by a source classifier are often misclassified as well …