Backpropagation, similar to most learning algorithms that can form complex decision surfaces, is prone to overfitting. This work presents classification-based objective functions, ...
Combining word alignments trained in two translation directions has mostly relied on heuristics that are not directly motivated by intended applications. We propose a novel method...
Abstract. Bayesian inference provides a powerful framework to optimally integrate statistically learned prior knowledge into numerous computer vision algorithms. While the Bayesian...
In this article we report on applications and extensions of weighted graph theory in the design and control of communication networks. We model the communication network as a weig...
—This paper presents a method for learning decision theoretic models of human behaviors from video data. Our system learns relationships between the movements of a person, the co...