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EMC$^2$: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence
We follow the global contrastive learning loss as introduced in SogCLR, and propose EMC$^2$ which utilizes an adaptive Metropolis-Hastings subroutine to generate hardness-aware negative samples in an online fashion during the optimization.
DoCoM: Compressed Decentralized Optimization with Near-Optimal Sample Complexity
This paper proposes the Doubly Compressed Momentum-assisted stochastic gradient tracking algorithm $\texttt{DoCoM}$ for communication-efficient decentralized optimization.
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