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» Language Learning from Stochastic Input
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NIPS
2007
14 years 9 days ago
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun
ALT
2003
Springer
14 years 2 months ago
Can Learning in the Limit Be Done Efficiently?
Abstract. Inductive inference can be considered as one of the fundamental paradigms of algorithmic learning theory. We survey results recently obtained and show their impact to pot...
Thomas Zeugmann
ICDAR
2003
IEEE
14 years 4 months ago
Parsing N-Best Lists of Handwritten Sentences
This paper investigates the application of a probabilistic parser for natural language on the list of the Nbest sentences produced by an off-line recognition system for cursive h...
Matthias Zimmermann, Jean-Cédric Chappelier...
ICASSP
2009
IEEE
13 years 8 months ago
Spoken language interpretation: On the use of dynamic Bayesian networks for semantic composition
In the context of spoken language interpretation, this paper introduces a stochastic approach to infer and compose semantic structures. Semantic frame structures are directly deri...
Marie-Jean Meurs, Fabrice Lefevre, Renato de Mori
VLDB
1998
ACM
110views Database» more  VLDB 1998»
14 years 3 months ago
Massive Stochastic Testing of SQL
Deterministic testing of SQL database systems is human intensive and cannot adequately cover the SQL input domain. A system (RAGS), was built to stochastically generate valid SQL ...
Donald R. Slutz