We present a model for sentence compression that uses a discriminative largemargin learning framework coupled with a novel feature set defined on compressed bigrams as well as dee...
Classification algorithms typically induce population-wide models that are trained to perform well on average on expected future instances. We introduce a Bayesian framework for l...
This paper presents a survey of techniques to implement multiplications by constants on FPGAs. It shows in particular that a simple and well-known technique, canonical signed recod...
Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in...
This paper deals with a category of concavifiable functions that can be used to model inelastic traffic in the network. Such class of functions can be concavified within an interva...