Many problems in computer vision can be modeled using
conditional Markov random fields (CRF). Since finding
the maximum a posteriori (MAP) solution in such models
is NP-hard, mu...
Stephen Gould (Stanford University), Fernando Amat...
Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by cl...
Insights into branch predictor organization and operation can be used in architecture-aware compiler optimizations to improve program performance. Unfortunately, such details are ...
Given a planar set S of arbitrary topology and a radius r, we show how to construct an r-tightening of S, which is a set whose boundary has a radius of curvature everywhere greate...
Whole program paths (WPP) are a new approach to capturing and representing a program’s dynamic—actually executed—control flow. Unlike other path profiling techniques, which ...