We propose a new neural network architecture, called Simple Recurrent Temporal-Difference Networks (SR-TDNs), that learns to predict future observations in partially observable en...
This work deals with reconfigurable control functions and protocols for supporting mobile computing applications in heterogeneous wireless systems like cellular networks and WLANs...
Traditional hop-by-hop dynamic routing makes inefficient use of network resources as it forwards packets along already congested shortest paths while uncongested longer paths may b...
Minsoo Lee, Xiaohui Ye, Dan Marconett, Samuel John...
We present the design and development of a Visual Learning Engine, a tool that can form the basis for interactive development of visually rich teaching and learning modules across...
In recent years there has been a flurry of works on learning probabilistic belief networks. Current state of the art methods have been shown to be successful for two learning scen...