Log In Sign Up

The many faces of deep learning

by   Raul Vicente, et al.

Deep learning has sparked a network of mutual interactions between different disciplines and AI. Naturally, each discipline focuses and interprets the workings of deep learning in different ways. This diversity of perspectives on deep learning, from neuroscience to statistical physics, is a rich source of inspiration that fuels novel developments in the theory and applications of machine learning. In this perspective, we collect and synthesize different intuitions scattered across several communities as for how deep learning works. In particular, we will briefly discuss the different perspectives that disciplines across mathematics, physics, computation, and neuroscience take on how deep learning does its tricks. Our discussion on each perspective is necessarily shallow due to the multiple views that had to be covered. The deepness in this case should come from putting all these faces of deep learning together in the reader's mind, so that one can look at the same problem from different angles.


page 1

page 2

page 3

page 4


Opening the black box of deep learning

The great success of deep learning shows that its technology contains pr...

A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives

Extracting behavioral measurements non-invasively from video is stymied ...

AI Safety for High Energy Physics

The field of high-energy physics (HEP), along with many scientific disci...

Nonparametric Universal Copula Modeling

To handle the ubiquitous problem of "dependence learning," copulas are q...

Como funciona o Deep Learning

Deep Learning methods are currently the state-of-the-art in many problem...

Deep Learning for Genomics: A Concise Overview

Advancements in genomic research such as high-throughput sequencing tech...

Automated Neuroscience Decision Support Framework

Psychophysiological chronic disorders lead to both physical disorders an...