NIPS
2007
13 years 11 months ago
2007
Stability is a desirable characteristic for linear dynamical systems, but it is often ignored by algorithms that learn these systems from data. We propose a novel method for learn...
NIPS
2007
13 years 11 months ago
2007
Hierarchical penalization is a generic framework for incorporating prior information in the fitting of statistical models, when the explicative variables are organized in a hiera...
NIPS
2007
13 years 11 months ago
2007
NIPS
2007
13 years 11 months ago
2007
We introduce a new Bayesian model for hierarchical clustering based on a prior over trees called Kingman’s coalescent. We develop novel greedy and sequential Monte Carlo inferen...
NIPS
2007
13 years 11 months ago
2007
We present a novel paradigm for statistical machine translation (SMT), based on a joint modeling of word alignment and the topical aspects underlying bilingual document-pairs, via...
NIPS
2007
13 years 11 months ago
2007
We describe a neurobiologically plausible model to implement dynamic routing using the concept of neuronal communication through neuronal coherence. The model has a three-tier arc...
NIPS
2007
13 years 11 months ago
2007
This paper studies boosting algorithms that make a single pass over a set of base classifiers. We first analyze a one-pass algorithm in the setting of boosting with diverse base...
NIPS
2007
13 years 11 months ago
2007
We propose a novel method for linear dimensionality reduction of manifold modeled data. First, we show that with a small number M of random projections of sample points in RN belo...
NIPS
2007
13 years 11 months ago
2007
In many applications, one has to actively select among a set of expensive observations before making an informed decision. Often, we want to select observations which perform well...
NIPS
2007
13 years 11 months ago
2007
Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...