An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

We survey and summarize the literature on the computational aspects of neural network
models by presenting a detailed taxonomy of the various models according to their …

We show that there is a simple (approximately radial) function on\mathbbR^ d, expressible
by a small 3-layer feedforward neural networks, which cannot be approximated by any 2 …

In this work, we focus on the challenge of taking partial observations of highly-stylized text
and generalizing the observations to generate unobserved glyphs in the ornamented …

Inventors have long dreamed of creating machines that think. Ancient Greek myths tell of
intelligent objects, such as animated statues of human beings and tables that arrive full of …

It has long been conjectured that hypotheses spaces suitable for data that is compositional
in nature, such as text or images, may be more efficiently represented with deep hierarchical …

The impressive capabilities of the mammalian brain—ranging from perception, pattern
recognition and memory formation to decision making and motor activity control—have …

In this article we survey the theory of two-party communication complexity. This field of
theoretical computer science aims at studying the following, seemingly very simple …

This paper explores the complexity of deep feedforward networks with linear pre-synaptic
couplings and rectified linear activations. This is a contribution to the growing body of work …

Theoretical results concerning the capabilities and limitations of various neural network
models are summarized, and some of their extensions are discussed. The network models …