Methods for imputation of missing data in the so-called least-squares approximation approach, a non-parametric computationally efficient multidimensional technique, are experiment...
A silent self-stabilizing asynchronous distributed algorithms is given for constructing a kdominating set, and hence a k-clustering, of a connected network of processes with uniqu...
Ajoy Kumar Datta, Lawrence L. Larmore, Priyanka Ve...
Abstract. Learning algorithms relying on Gibbs sampling based stochastic approximations of the log-likelihood gradient have become a common way to train Restricted Boltzmann Machin...
In the longest common subsequence problem the task is to find the longest sequence of letters that can be found as subsequence in all members of a given finite set of sequences....
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...