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» Learning to Learn Causal Models
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151
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AAMAS
2005
Springer
15 years 6 months ago
Coordinating Multiple Agents via Reinforcement Learning
In this paper, we focus on the coordination issues in a multiagent setting. Two coordination algorithms based on reinforcement learning are presented and theoretically analyzed. O...
Gang Chen, Zhonghua Yang, Hao He, Kiah Mok Goh
208
Voted
ICASSP
2010
IEEE
15 years 5 months ago
Multiple sequence alignment based bootstrapping for improved incremental word learning
We investigate incremental word learning with few training examples in a Hidden Markov Model (HMM) framework suitable for an interactive learning scenario with little prior knowle...
Irene Ayllól Clemente, Martin Heckmann, Ger...
174
Voted
ACL
2010
15 years 4 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...
188
Voted
NAACL
2010
15 years 4 months ago
Learning Words and Their Meanings from Unsegmented Child-directed Speech
Most work on language acquisition treats word segmentation--the identification of linguistic segments from continuous speech-and word learning--the mapping of those segments to me...
Bevan K. Jones, Mark Johnson, Michael C. Frank
ICASSP
2011
IEEE
14 years 10 months ago
Sparse coding and dictionary learning based on the MDL principle
The power of sparse signal coding with learned overcomplete dictionaries has been demonstrated in a variety of applications and fields, from signal processing to statistical infe...
Ignacio Ramírez, Guillermo Sapiro