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ICML
2008
IEEE
14 years 5 months ago
Extracting and composing robust features with denoising autoencoders
Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to use...
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pi...
SCVMA
2004
Springer
13 years 10 months ago
A Generative Model of Dense Optical Flow in Layers
We introduce a generative model of dense flow fields within a layered representation of 3-dimensional scenes. Using probabilistic inference and learning techniques (namely, varia...
Anitha Kannan, Brendan J. Frey, Nebojsa Jojic
EUROPAR
2005
Springer
13 years 10 months ago
SPC-XML: A Structured Representation for Nested-Parallel Programming Languages
Nested-parallelism programming models, where the task graph associated to a computation is series-parallel, present good analysis properties that can be exploited for scheduling, c...
Arturo González-Escribano, Arjan J. C. van ...
COLING
2002
13 years 4 months ago
A New Probabilistic Model for Title Generation
Title generation is a complex task involving both natural language understanding and natural language synthesis. In this paper, we propose a new probabilistic model for title gene...
Rong Jin, Alexander G. Hauptmann
COLING
2000
13 years 6 months ago
Exploiting a Probabilistic Hierarchical Model for Generation
Previous stochastic approaches to generation do not include a tree-based representation of syntax. While this may be adequate or even advantageous for some applications, other app...
Srinivas Bangalore, Owen Rambow