Attributes are visual concepts that can be detected by machines, understood by humans, and shared across categories. They are particularly useful for fine-grained domains where c...
Kun Duan, Devi Parikh, David J. Crandall, Kristen ...
We present a new approximate inference algorithm for Deep Boltzmann Machines (DBM's), a generative model with many layers of hidden variables. The algorithm learns a separate...
We present a algorithm based on factor analysis for performing collaborative quality filtering (CQF). Unlike previous approaches to CQF, which estimate the consensus opinion of a...
We study a new task, proactive information retrieval by combining implicit relevance feedback and collaborative filtering. We have constructed a controlled experimental setting, ...
This paper establishes a connection between two apparently very different kinds of probabilistic models. Latent Dirichlet Allocation (LDA) models are used as "topic models&qu...