19 citations · 37 across the 8 of their papers we have counts for
12 papers
Rethinking gradient sparsification as total error minimization
Atal Narayan Sahu, Aritra Dutta, Ahmed M. Abdelmoniem +3
Gradient compression is a widely-established remedy to tackle the communication bottleneck in distributed training of large deep neural networks (DNNs). Under the error-feedback fr…
DeepReduce: A Sparse-tensor Communication Framework for Distributed Deep Learning
Kelly Kostopoulou, Hang Xu, Aritra Dutta +3
Sparse tensors appear frequently in distributed deep learning, either as a direct artifact of the deep neural network's gradients, or as a result of an explicit sparsification proc…
On the Convergence Analysis of Asynchronous SGD for Solving Consistent Linear Systems
Atal Narayan Sahu, Aritra Dutta, Aashutosh Tiwari +1
In the realm of big data and machine learning, data-parallel, distributed stochastic algorithms have drawn significant attention in the present days.~While the synchronous versions…
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning
Aritra Dutta, El Houcine Bergou, Ahmed M. Abdelmoniem +4
Compressed communication, in the form of sparsification or quantization of stochastic gradients, is employed to reduce communication costs in distributed data-parallel training of…
Direct Nonlinear Acceleration
Aritra Dutta, El Houcine Bergou, Yunming Xiao +2
Optimization acceleration techniques such as momentum play a key role in state-of-the-art machine learning algorithms. Recently, generic vector sequence extrapolation techniques, s…
Best Pair Formulation & Accelerated Scheme for Non-convex Principal Component Pursuit
Aritra Dutta, Filip Hanzely, Jingwei Liang +1
The best pair problem aims to find a pair of points that minimize the distance between two disjoint sets. In this paper, we formulate the classical robust principal component analy…