Reinforcement Learning research is traditionally devoted to solve single-task problems. Therefore, anytime a new task is faced, learning must be restarted from scratch. Recently, ...
Let G = (V, E, Q) be a undirected graph, where V is the set of vertices, E is the set of edges, and Q = {Q1, . . . , Qq} is a partition of V into q subsets. We refer to Q1, . . . ...
Yuri Frota, Nelson Maculan, Thiago F. Noronha, Cel...
— We come up with novel quantized averaging algorithms on synchronous and asynchronous communication networks with fixed, switching and random topologies. The implementation of ...
Abstract. We study strategies that minimize the instability of a faulttolerant consensus system. More precisely, we find the strategy than minimizes the number of output changes ov...
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...