Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence
The authors propose an Efficient Markov Chain Monte Carlo (EMC2) negative sampling method for contrastive learning that exhibits global convergence to a stationary point, regardless of the choice of batch size.
