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» Modelling Smooth Paths Using Gaussian Processes
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ICML
2005
IEEE
14 years 8 months ago
Active learning for Hidden Markov Models: objective functions and algorithms
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...
Brigham Anderson, Andrew Moore
JMLR
2002
137views more  JMLR 2002»
13 years 7 months ago
The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces
A central problem in learning is selection of an appropriate model. This is typically done by estimating the unknown generalization errors of a set of models to be selected from a...
Masashi Sugiyama, Klaus-Robert Müller
NIPS
2008
13 years 9 months ago
Natural Image Denoising with Convolutional Networks
We present an approach to low-level vision that combines two main ideas: the use of convolutional networks as an image processing architecture and an unsupervised learning procedu...
Viren Jain, H. Sebastian Seung
CORR
2010
Springer
77views Education» more  CORR 2010»
13 years 7 months ago
Stochastic Analysis of Non-slotted Aloha in Wireless Ad-Hoc Networks
: In this paper we propose two analytically tractable stochastic models of non-slotted Aloha for Mobile Ad-hoc NETworks (MANETs): one model assumes a static pattern of nodes while ...
Bartek Blaszczyszyn, Paul Mühlethaler
ICASSP
2011
IEEE
12 years 11 months ago
Integrating binaural cues and blind source separation method for separating reverberant speech mixtures
This paper presents a new method for reverberant speech separation, based on the combination of binaural cues and blind source separation (BSS) for the automatic classification o...
Atiyeh Alinaghi, Wenwu Wang, Philip J. B. Jackson