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» Learning for stochastic dynamic programming
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BIOCOMP
2006
15 years 4 months ago
Acceleration of Covariance Models for Non-coding RNA Search
Stochastic context-free grammar (SCFG) based models for non-coding RNA (ncRNA) gene searches are much more powerful than regular grammar based models due to the ability to model in...
Scott F. Smith 0002
CORR
2010
Springer
123views Education» more  CORR 2010»
14 years 9 months ago
Equilibria of Dynamic Games with Many Players: Existence, Approximation, and Market Structure
In this paper we study stochastic dynamic games with many players that are relevant for a wide range of social, economic, and engineering applications. The standard solution conce...
Sachin Adlakha, Ramesh Johari, Gabriel Y. Weintrau...
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...
137
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JMLR
2012
13 years 5 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
ICDM
2007
IEEE
157views Data Mining» more  ICDM 2007»
15 years 4 months ago
Training Conditional Random Fields by Periodic Step Size Adaptation for Large-Scale Text Mining
For applications with consecutive incoming training examples, on-line learning has the potential to achieve a likelihood as high as off-line learning without scanning all availabl...
Han-Shen Huang, Yu-Ming Chang, Chun-Nan Hsu