Abstract— We propose a planning algorithm that allows usersupplied domain knowledge to be exploited in the synthesis of information feedback policies for systems modeled as parti...
Salvatore Candido, James C. Davidson, Seth Hutchin...
Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statisti...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
Reinforcement learning algorithms that employ neural networks as function approximators have proven to be powerful tools for solving optimal control problems. However, their traini...
Many problems require repeated inference on probabilistic graphical models, with different values for evidence variables or other changes. Examples of such problems include utilit...
Abstract--We show that the maximization of the sum degreesof-freedom for the static flat-fading multiple-input multipleoutput (MIMO) interference channel is equivalent to a rank co...