This paper describes a novel application of Statistical Learning Theory (SLT) for motion prediction. SLT provides analytical VC-generalization bounds for model selection; these bo...
Harry Wechsler, Zoran Duric, Fayin Li, Vladimir Ch...
Based on biological data we examine the ability of Support Vector Machines (SVMs) with gaussian kernels to learn and predict the nonlinear dynamics of single biological neurons. We...
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...
In this paper, we propose a cache design that provides the same miss rate as a two-way set associative cache, but with a access time closer to a direct-mapped cache. As with other...
Branch instructions are recognized as a major impediment to exploiting instruction level parallelism. Even with sophisticated branch prediction techniques, many frequently execute...
Scott A. Mahlke, Richard E. Hank, Roger A. Bringma...