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ICASSP
2010
IEEE
15 years 3 months ago
Power law discounting for n-gram language models
We present an approximation to the Bayesian hierarchical PitmanYor process language model which maintains the power law distribution over word tokens, while not requiring a comput...
Songfang Huang, Steve Renals
COGSCI
2010
99views more  COGSCI 2010»
15 years 3 months ago
Learning to Learn Causal Models
Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical B...
Charles Kemp, Noah D. Goodman, Joshua B. Tenenbaum
PAA
2008
15 years 3 months ago
Human action recognition in videos based on the Transferable Belief Model
Abstract This paper focuses on human behavior recognition where the main problem is to bridge the semantic gap between the analogue observations of the real world and the symbolic ...
Emmanuel Ramasso, Costas Panagiotakis, Denis Pelle...
ECCV
2002
Springer
16 years 5 months ago
Audio-Video Sensor Fusion with Probabilistic Graphical Models
Abstract. We present a new approach to modeling and processing multimedia data. This approach is based on graphical models that combine audio and video variables. We demonstrate it...
Matthew J. Beal, Hagai Attias, Nebojsa Jojic
ICML
2009
IEEE
16 years 4 months ago
Learning structurally consistent undirected probabilistic graphical models
In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the dependency structure than directed graphical mode...
Sushmita Roy, Terran Lane, Margaret Werner-Washbur...