32 citations · 64 across the 26 of their papers we have counts for
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cs.LG2022
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods
Yuchen Lu, Zhen Liu, Aristide Baratin +3
We address the problem of evaluating the quality of self-supervised learning (SSL) models without access to supervised labels, while being agnostic to the architecture, learning al…
cs.LG2022★ 3 cited
GraB: Finding Provably Better Data Permutations than Random Reshuffling
Yucheng Lu, Wentao Guo, Christopher De Sa
Random reshuffling, which randomly permutes the dataset each epoch, is widely adopted in model training because it yields faster convergence than with-replacement sampling. Recent…
cs.LG2022★ 4 cited
Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam
Yucheng Lu, Conglong Li, Minjia Zhang +2
1-bit gradient compression and local steps are two representative techniques that enable drastic communication reduction in distributed SGD. Their benefits, however, remain an open…