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JMLR
2008
230views more  JMLR 2008»
14 years 9 months ago
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
Michael Collins, Amir Globerson, Terry Koo, Xavier...
CVPR
2006
IEEE
15 years 3 months ago
Fast Variational Segmentation using Partial Extremal Initialization
In this paper we consider region-based variational segmentation of two- and three-dimensional images by the minimization of functionals whose fidelity term is the quotient of two...
Jan Erik Solem, Niels Chr. Overgaard, Markus Perss...
JMLR
2012
12 years 12 months ago
Deep Learning Made Easier by Linear Transformations in Perceptrons
We transform the outputs of each hidden neuron in a multi-layer perceptron network to have zero output and zero slope on average, and use separate shortcut connections to model th...
Tapani Raiko, Harri Valpola, Yann LeCun
WSC
2004
14 years 10 months ago
Stochastic Approximation with Simulated Annealing as an Approach to Global Discrete-Event Simulation Optimization
This paper explores an approach to global, stochastic, simulation optimization which combines stochastic approximation (SA) with simulated annealing (SAN). SA directs a search of ...
Matthew H. Jones, K. Preston White
IJCAI
2001
14 years 10 months ago
Solving Non-Boolean Satisfiability Problems with Stochastic Local Search
Much excitement has been generated by the success of stochastic local search procedures at finding solutions to large, very hard satisfiability problems. Many of the problems on wh...
Alan M. Frisch, Timothy J. Peugniez