Echo State Networks (ESNs) have been shown to be effective for a number of tasks, including motor control, dynamic time series prediction, and memorizing musical sequences. Howeve...
Matthew H. Tong, Adam D. Bickett, Eric M. Christia...
-- This paper presents the current state of a work in progress, whose objective is to better understand the effects of factors that significantly influence the performance of the L...
Alain Lifchitz, Sandra Jhean-Larose, Guy Denhi&egr...
This paper explores the correlation of instruction counts and cache misses to runtime performance for a large family of divide and conquer algorithms to compute the Walsh–Hadama...
We study the randomized version of a computation model (introduced in [9, 10]) that restricts random access to external memory and internal memory space. Essentially, this model c...
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...