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ICML
2010
IEEE
13 years 11 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...
NECO
2010
136views more  NECO 2010»
13 years 8 months ago
Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines
To allow the hidden units of a restricted Boltzmann machine to model the transformation between two successive images, Memisevic and Hinton (2007) introduced three-way multiplicat...
Roland Memisevic, Geoffrey E. Hinton
SAB
2010
Springer
226views Optimization» more  SAB 2010»
13 years 8 months ago
Distributed Online Learning of Central Pattern Generators in Modular Robots
Abstract. In this paper we study distributed online learning of locomotion gaits for modular robots. The learning is based on a stochastic approximation method, SPSA, which optimiz...
David Johan Christensen, Alexander Spröwitz, ...
CVPR
2011
IEEE
13 years 8 months ago
Dynamic Batch Mode Active Learning
Active learning techniques have gained popularity in reducing human effort to annotate data instances for inducing a classifier. When faced with large quantities of unlabeled dat...
Shayok Chakraborty, Vineeth Balasubramanian, Sethu...
AIPS
2008
14 years 20 days ago
An Online Learning Method for Improving Over-Subscription Planning
Despite the recent resurgence of interest in learning methods for planning, most such efforts are still focused exclusively on classical planning problems. In this work, we invest...
Sung Wook Yoon, J. Benton, Subbarao Kambhampati