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Found 7 papers, 3 papers with code
PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
It is widely acknowledged that large models have the potential to deliver superior performance across a broad range of domains.
PyTorch Distributed: Experiences on Accelerating Data Parallel Training
This paper presents the design, implementation, and evaluation of the PyTorch distributed data parallel module.
Vector-Valued Graph Trend Filtering with Non-Convex Penalties
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.
Sampling Theory for Graph Signals on Product Graphs
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 graph signals that lie on them.
Signal Representations on Graphs: Tools and Applications
For each class, we provide an explicit definition of the graph signals and construct a corresponding graph dictionary with desirable properties.
A statistical perspective of sampling scores for linear regression
In this paper, we consider a statistical problem of learning a linear model from noisy samples.
Signal Recovery on Graphs: Random versus Experimentally Designed Sampling
We study signal recovery on graphs based on two sampling strategies: random sampling and experimentally designed sampling.
