908 citations · 2.4k across the 149 of their papers we have counts for
4 papers · 2 filters
Asynchronous Decentralized SGD with Quantized and Local Updates
Giorgi Nadiradze, Amirmojtaba Sabour, Peter Davies +2
Decentralized optimization is emerging as a viable alternative for scalable distributed machine learning, but also introduces new challenges in terms of synchronization costs. To t…
Powerset Convolutional Neural Networks
Chris Wendler, Dan Alistarh, Markus Püschel
We present a novel class of convolutional neural networks (CNNs) for set functions, i.e., data indexed with the powerset of a finite set. The convolutions are derived as linear, sh…
NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization
Ali Ramezani-Kebrya, Fartash Faghri, Ilya Markov +3
As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed to perform paralle…
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…