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ICML
2010
IEEE
13 years 11 months ago
Bottom-Up Learning of Markov Network Structure
The structure of a Markov network is typically learned using top-down search. At each step, the search specializes a feature by conjoining it to the variable or feature that most ...
Jesse Davis, Pedro Domingos
AAI
2005
93views more  AAI 2005»
13 years 10 months ago
Learning By Feeling: Evoking Empathy With Synthetic Characters
Virtual environments are now becoming a promising new technology to be used in the development of interactive learning environments for children. Perhaps triggered by the success ...
Ana Paiva, João Dias, Daniel Sobral, Ruth A...
CONNECTION
2004
98views more  CONNECTION 2004»
13 years 9 months ago
Self-refreshing memory in artificial neural networks: learning temporal sequences without catastrophic forgetting
While humans forget gradually, highly distributed connectionist networks forget catastrophically: newly learned information often completely erases previously learned information. ...
Bernard Ans, Stephane Rousset, Robert M. French, S...
ACL
2010
13 years 8 months ago
Reading between the Lines: Learning to Map High-Level Instructions to Commands
In this paper, we address the task of mapping high-level instructions to sequences of commands in an external environment. Processing these instructions is challenging--they posit...
S. R. K. Branavan, Luke S. Zettlemoyer, Regina Bar...
TSMC
2011
210views more  TSMC 2011»
13 years 4 months ago
Fault Diagnosis in Discrete-Event Systems: Incomplete Models and Learning
— Most state-based approaches to fault diagnosis of discrete-event systems require a complete and accurate model of the system to be diagnosed. In this paper, we address the prob...
Raymond H. Kwong, David L. Yonge-Mallo