Abstract. Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high-dimensional, continuous control problems. We propose to exploit API for...
Detecting objects in complex scenes while recovering the scene layout is a critical functionality in many vision-based applications. Inspired by the work of [18], we advocate the ...
— We consider whether the off-line compilation of a set of Service Level Agreements (SLAs) into low-level management policies can lead to the runtime maximization of the overall ...
We investigate the question of when a prover can aid a verifier to reliably compute a function faster than if the verifier were to compute the function on its own. Our focus is ...
We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse,...