Robotic controllers take advantage from neural network learning capabilities as long as the dimensionality of the problem is kept moderate. This paper explores the possibilities of...
This paper studies the use of discrete-time recurrent neural networks for predicting the next symbol in a sequence. The focus is on online prediction, a task much harder than the c...
This paper presents Synapse, a scalable protocol for information retrieval over the inter-connection of heterogeneous overlay networks. Applications on top of Synapse see those int...
—We present a methodology based on physics laws and particles in order to represent, simulate, and architect advanced networking models. We introduce a mathematical formalism wit...
This paper presents a new framework for drawing graphs in three dimensions. In general, the new framework uses a divide and conquer approach. More specifically, the framework divi...