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114
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ICML
2004
IEEE
16 years 3 months ago
Training conditional random fields via gradient tree boosting
Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such appl...
Thomas G. Dietterich, Adam Ashenfelter, Yaroslav B...
NIPS
1990
15 years 3 months ago
Back Propagation is Sensitive to Initial Conditions
This paper explores the effect of initial weight selection on feed-forward networks learning simple functions with the back-propagation technique. We first demonstrate, through th...
John F. Kolen, Jordan B. Pollack
MR
2007
96views Robotics» more  MR 2007»
15 years 1 months ago
A step by step methodology to analyze the IGBT failure mechanisms under short circuit and turn-off inductive conditions using 2D
A systematic methodology is developed in order to clarify the punch through Trench Insulated Gate Bipolar Transistor (T-IGBT) failure mechanisms which can occur under extreme oper...
A. Benmansour, Stephane Azzopardi, J. C. Martin, E...
ICML
2006
IEEE
16 years 3 months ago
Accelerated training of conditional random fields with stochastic gradient methods
We apply Stochastic Meta-Descent (SMD), a stochastic gradient optimization method with gain vector adaptation, to the training of Conditional Random Fields (CRFs). On several larg...
S. V. N. Vishwanathan, Nicol N. Schraudolph, Mark ...
123
Voted
COCOON
2009
Springer
15 years 6 months ago
Optimal Transitions for Targeted Protein Quantification: Best Conditioned Submatrix Selection
Multiple reaction monitoring (MRM) is a mass spectrometric method to quantify a specified set of proteins. In this paper, we identify a problem at the core of MRM peptide quantific...
Rastislav Srámek, Bernd Fischer, Elias Vica...