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stat.ML2021
Contrastive learning of strong-mixing continuous-time stochastic processes
Bingbin Liu, Pradeep Ravikumar, Andrej Risteski
Contrastive learning is a family of self-supervised methods where a model is trained to solve a classification task constructed from unlabeled data. It has recently emerged as one…
stat.ML2019
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
Rares-Darius Buhai, Yoni Halpern, Yoon Kim +2
One of the most surprising and exciting discoveries in supervised learning was the benefit of overparameterization (i.e. training a very large model) to improving the optimization…