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AAAI
1992
15 years 3 months ago
Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning
It is often useful for a robot to construct a spatial representation of its environment from experiments and observations, in other words, to learn a map of its environment by exp...
Thomas Dean, Dana Angluin, Kenneth Basye, Sean P. ...
ICML
2010
IEEE
15 years 3 months ago
Learning Efficiently with Approximate Inference via Dual Losses
Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation...
Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Glo...
ICFP
2005
ACM
16 years 2 months ago
Type inference, principal typings, and let-polymorphism for first-class mixin modules
module is a programming abstraction that simultaneously generalizes -abstractions, records, and mutually recursive definitions. Although various mixin module type systems have bee...
Henning Makholm, J. B. Wells
NECO
2007
127views more  NECO 2007»
15 years 1 months ago
Visual Recognition and Inference Using Dynamic Overcomplete Sparse Learning
We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and e...
Joseph F. Murray, Kenneth Kreutz-Delgado
ICANN
2010
Springer
15 years 3 months ago
Variational Bayesian Image Super-Resolution with GPU Acceleration
With the term super-resolution we refer to the problem of reconstructing an image of higher resolution than that of unregistered and degraded observations. Typically, the reconstru...
Giannis K. Chantas