Efficient Second-order Optimization for Machine Learning
Stochastic gradient-based methods are the state-of-the-art in large-scale machine learning optimization due to their extremely efficient per-iteration computational cost. Second-order methods, that use the second derivative of the optimization objective, are known to enable faster…
From Deep Learning of Disentangled Representations to Higher-level Cognition
Abstract One of the main challenges for AI remains unsupervised learning, at which humans are much better than machines, and which we link to another challenge: bringing deep learning to higher-level cognition. We review earlier…
Frontiers in AI – Francis Bach
Frontiers in Artificial Intelligence is a series of public lectures at Microsoft Research Cambridge featur-ing leading researchers in the field, focusing on the cutting edge topics at the intersection of machine learning, statistics, and artificial…