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Found 3 papers, 1 papers with code
Learning Mathematical Rules with Large Language Models
In this paper, we study the ability of large language models to learn specific mathematical rules such as distributivity or simplifying equations.
Correlated Quantization for Faster Nonconvex Distributed Optimization
We analyze the forefront distributed non-convex optimization algorithm MARINA (Gorbunov et al., 2022) utilizing the proposed correlated quantizers and show that it outperforms the original MARINA and distributed SGD of Suresh et al. (2022) with regard to the communication complexity.
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates
Byzantine robustness is an essential feature of algorithms for certain distributed optimization problems, typically encountered in collaborative/federated learning.
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