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Found 5 papers, 1 papers with code
BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory
We consider feature selection for applications in machine learning where the dimensionality of the data is so large that it exceeds the working memory of the (local) computing machine.
FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning
In this paper, we propose a secure aggregation protocol, FastSecAgg, that is efficient in terms of computation and communication, and robust to client dropouts.
Communication-Efficient Gradient Coding for Straggler Mitigation in Distributed Learning
When a particular code is used in this framework, its block-length determines the computation load, dimension determines the communication overhead, and minimum distance determines the straggler tolerance.
Gradient Coding Based on Block Designs for Mitigating Adversarial Stragglers
In this work, our goal is to construct approximate gradient codes that are resilient to stragglers selected by a computationally unbounded adversary.
On the organization of grid and place cells: Neural de-noising via subspace learning
The joint activity of grid and place cell populations, as a function of location, forms a neural code for space.
