RESEARCH

SPEECH & AUDIO

Learning graphs from data: A signal representation perspective

May 1, 2019

Abstract

The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis and visualization of structured data. When a natural choice of the graph is not readily available from the datasets, it is thus desirable to infer or learn a graph topology from the data. In this tutorial overview, we survey solutions to the problem of graph learning, including classical viewpoints from statistics and physics, and more recent approaches that adopt a graph signal processing (GSP) perspective. We further emphasize the conceptual similarities and differences between classical and GSP-based graph inference methods, and highlight the potential advantage of the latter in a number of theoretical and practical scenarios. We conclude with several open issues and challenges that are keys to the design of future signal processing and machine learning algorithms for learning graphs from data.

Download the Paper

Related Publications

September 15, 2019

SPEECH & AUDIO

Who Needs Words? Lexicon-Free Speech Recognition | Facebook AI Research

Tatiana Likhomanenko, Gabriel Synnaeve, Ronan Collobert

September 15, 2019

July 28, 2019

SPEECH & AUDIO

COMPUTER VISION

Learning to Optimize Halide with Tree Search and Random Programs | Facebook AI Research

Andrew Adams, Karima Ma, Luke Anderson, Riyadh Baghdadi, Tzu-Mao Li, Michaël Gharbi, Benoit Steiner, Steven Johnson, Kayvon Fatahalian, Frédo Durand, Jonathan Ragan-Kelley

July 28, 2019

March 11, 2019

SPEECH & AUDIO

Stochastic Adaptive Neural Architecture Search for Keyword Spotting | Facebook AI Research

Tom Véniat, Olivier Schwander, Ludovic Denoyer

March 11, 2019

May 01, 2019

SPEECH & AUDIO

Learning graphs from data: A signal representation perspective | Facebook AI Research

Xiaowen Dong, Dorina Thanou, Michael Rabbat, Pascal Frossard

May 01, 2019

Related Work

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.