56 citations · 243 across the 35 of their papers we have counts for
3 papers · 1 filter
Deep Mean Maps
Junier B. Oliva, Danica J. Sutherland, Barnabás Póczos +1
The use of distributions and high-level features from deep architecture has become commonplace in modern computer vision. Both of these methodologies have separately achieved a gre…
Linear-time Learning on Distributions with Approximate Kernel Embeddings
Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos +1
Many interesting machine learning problems are best posed by considering instances that are distributions, or sample sets drawn from distributions. Previous work devoted to machine…
Bayesian Nonparametric Kernel-Learning
Junier Oliva, Avinava Dubey, Andrew G. Wilson +3
Kernel methods are ubiquitous tools in machine learning. However, there is often little reason for the common practice of selecting a kernel a priori. Even if a universal approxima…