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Autoencoder-based Graph Construction for Semi-supervised Learning
We consider graph-based semi-supervised learning that leverages a similarity graph across data points to better exploit data structure exposed in unlabeled data.
Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions
We analyze the dynamics of large batch stochastic gradient descent with momentum (SGD+M) on the least squares problem when both the number of samples and dimensions are large.
SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality
We propose a new framework, inspired by random matrix theory, for analyzing the dynamics of stochastic gradient descent (SGD) when both number of samples and dimensions are large.
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