693 citations · 892 across the 7 of their papers we have counts for
9 papers
PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
Yanli Zhao, Andrew Gu, Rohan Varma +15
It is widely acknowledged that large models have the potential to deliver superior performance across a broad range of domains. Despite the remarkable progress made in the field of…
PyTorch Distributed: Experiences on Accelerating Data Parallel Training
Shen Li, Yanli Zhao, Rohan Varma +8
This paper presents the design, implementation, and evaluation of the PyTorch distributed data parallel module. PyTorch is a widely-adopted scientific computing package used in dee…
Vector-Valued Graph Trend Filtering with Non-Convex Penalties
Rohan Varma, Harlin Lee, Jelena Kovačević +1
This work studies the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph, where the value at each node can be vector-valued. W…
Sampling Theory for Graph Signals on Product Graphs
Rohan Varma, Jelena Kovačević
In this paper, we extend the sampling theory on graphs by constructing a framework that exploits the structure in product graphs for efficient sampling and recovery of bandlimited…
Signal Representations on Graphs: Tools and Applications
Siheng Chen, Rohan Varma, Aarti Singh +1
We present a framework for representing and modeling data on graphs. Based on this framework, we study three typical classes of graph signals: smooth graph signals, piecewise-const…
Signal Recovery on Graphs: Fundamental Limits of Sampling Strategies
Siheng Chen, Rohan Varma, Aarti Singh +1
This paper builds theoretical foundations for the recovery of a newly proposed class of smooth graph signals, approximately bandlimited graph signals, under three sampling strategi…