RESEARCH

Provably Accelerated Randomized Gossip Algorithms

May 12, 2019

Abstract

In this work we present novel provably accelerated gossip algorithms for solving the average consensus problem. The proposed protocols are inspired from the recently developed accelerated variants of the randomized Kaczmarz method – a popular method for solving linear systems. In each gossip iteration all nodes of the network update their values but only a pair of them exchange their private information. Numerical experiments on popular wireless sensor networks showing the benefits of our protocols are also presented.

Download the Paper

Related Publications

June 03, 2019

NLP

FAIRSEQ: A Fast, Extensible Toolkit for Sequence Modeling | Facebook AI Research

FAIRSEQ is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports…

Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli

June 03, 2019

June 02, 2019

NLP

Cooperative Learning of Disjoint Syntax and Semantics | Facebook AI Research

There has been considerable attention devoted to models that learn to jointly infer an expression’s syntactic structure and its semantics. Yet, Nangia and Bowman (2018) has recently shown that the current best systems fail to learn the correct…

Serhii Havrylov, Germán Kruszewski, Armand Joulin

June 02, 2019

June 15, 2019

COMPUTER VISION

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search | Facebook AI Research

Designing accurate and efficient ConvNets for mobile devices is challenging because the design space is combinatorially large. Due to this, previous neural architecture search (NAS) methods are computationally expensive. ConvNet architecture…

Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, Kurt Keutzer

June 15, 2019

April 28, 2019

COMPUTER VISION

Inverse Path Tracing for Joint Material and Lighting Estimation | Facebook AI Research

Modern computer vision algorithms have brought significant advancement to 3D geometry reconstruction. However, illumination and material reconstruction remain less studied, with current approaches assuming very simplified models for materials…

Dejan Azinović, Tzu-Mao Li, Anton Kaplanyan, Matthias Nießner

April 28, 2019

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.