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NPL
2006
85views more  NPL 2006»
14 years 10 months ago
A Neural Model for Context-dependent Sequence Learning
A novel neural network model is described that implements context-dependent learning of complex sequences. The model utilises leaky integrate-and-fire neurons to extract timing inf...
Luc Berthouze, Adriaan G. Tijsseling
ACSC
2004
IEEE
15 years 1 months ago
Learning Models for English Speech Recognition
This paper reports on an experiment to determine the optimal parameters for a speech recogniser that is part of a computer aided instruction system for assisting learners of Engli...
Huayang Xie, Peter Andreae, Mengjie Zhang, Paul Wa...
RAS
2007
122views more  RAS 2007»
14 years 9 months ago
Developmental learning for autonomous robots
Developmental robotics is concerned with the design of algorithms that promote robot adaptation and learning through qualitative growth of behaviour and increasing levels of compe...
M. H. Lee, Q. Meng, F. Chao
COLING
2010
14 years 5 months ago
Generative Alignment and Semantic Parsing for Learning from Ambiguous Supervision
We present a probabilistic generative model for learning semantic parsers from ambiguous supervision. Our approach learns from natural language sentences paired with world states ...
Joohyun Kim, Raymond J. Mooney
ACL
2009
14 years 7 months ago
Semi-supervised Learning of Dependency Parsers using Generalized Expectation Criteria
In this paper, we propose a novel method for semi-supervised learning of nonprojective log-linear dependency parsers using directly expressed linguistic prior knowledge (e.g. a no...
Gregory Druck, Gideon S. Mann, Andrew McCallum