Although Bayesian model averaging is theoretically the optimal method for combining learned models, it has seen very little use in machine learning. In this paper we study its app...
Architectures for optical processors designed to solve bounded instances of NP-Complete problems are suggested. One approach mimics the traveling salesman by traveling beams that ...
We describe an approach for exploiting structure in Markov Decision Processes with continuous state variables. At each step of the dynamic programming, the state space is dynamica...
Zhengzhu Feng, Richard Dearden, Nicolas Meuleau, R...
We present an algorithm for computing rigorous solutions to a large class of ordinary differential equations. The main algorithm is based on a partitioning process and the use of i...
Abstract--The generalized minimum vertex-biconnected network problem plays an important role in the design of survivable backbone networks that should be fault tolerant to single c...