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» Debugging Program Loops Using Approximate Modeling
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JMLR
2012
13 years 10 days ago
Krylov Subspace Descent for Deep Learning
In this paper, we propose a second order optimization method to learn models where both the dimensionality of the parameter space and the number of training samples is high. In ou...
Oriol Vinyals, Daniel Povey
CP
2010
Springer
14 years 8 months ago
A Box-Consistency Contractor Based on Extremal Functions
Abstract. Interval-based methods can approximate all the real solutions of a system of equations and inequalities. The Box interval constraint propagation algorithm enforces Box co...
Gilles Trombettoni, Yves Papegay, Gilles Chabert, ...
UAI
2004
14 years 11 months ago
Solving Factored MDPs with Continuous and Discrete Variables
Although many real-world stochastic planning problems are more naturally formulated by hybrid models with both discrete and continuous variables, current state-of-the-art methods ...
Carlos Guestrin, Milos Hauskrecht, Branislav Kveto...
TSMC
2008
177views more  TSMC 2008»
14 years 8 months ago
Adaptive Critic Learning Techniques for Engine Torque and Air-Fuel Ratio Control
A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning con...
Derong Liu, Hossein Javaherian, Olesia Kovalenko, ...
TSE
1998
116views more  TSE 1998»
14 years 9 months ago
A Framework-Based Approach to the Development of Network-Aware Applications
— Modern networks provide a QoS (quality of service) model to go beyond best-effort services, but current QoS models are oriented towards low-level network parameters (e.g., band...
Jürg Bolliger, Thomas R. Gross