Papers
Canonical Time Warping for Alignment of Human Behavior
Feng Zhou, Fernando Torre
Clustering sequence sets for motif discovery
Jong K. Kim, Seungjin Choi
Code-specific policy gradient rules for spiking neurons
Henning Sprekeler, Guillaume Hennequin, Wulfram Gerstner
Complexity of Decentralized Control: Special Cases
Martin Allen, Shlomo Zilberstein
Compositionality of optimal control laws
Emanuel Todorov
Compressed Least-Squares Regression
Odalric Maillard, Rémi Munos
Conditional Neural Fields
Jian Peng, Liefeng Bo, Jinbo Xu
Conditional Random Fields with High-Order Features for Sequence Labeling
Nan Ye, Wee S. Lee, Hai L. Chieu et al.
Constructing Topological Maps using Markov Random Fields and Loop-Closure Detection
Roy Anati, Kostas Daniilidis
Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation
Hamid R. Maei, Csaba Szepesvári, Shalabh Bhatnagar et al.
Convex Relaxation of Mixture Regression with Efficient Algorithms
Novi Quadrianto, John Lim, Dale Schuurmans et al.
Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies
Arno Onken, Steffen Grünewälder, Klaus Obermayer
Data-driven calibration of linear estimators with minimal penalties
Sylvain Arlot, Francis R. Bach
Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process
Chong Wang, David M. Blei
Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning
Anne Hsu, Thomas L. Griffiths
Directed Regression
Yi-hao Kao, Benjamin V. Roy, Xiang Yan
Dirichlet-Bernoulli Alignment: A Generative Model for Multi-Class Multi-Label Multi-Instance Corpora
Shuang-hong Yang, Hongyuan Zha, Bao-gang Hu
Discrete MDL Predicts in Total Variation
Marcus Hutter
Discriminative Network Models of Schizophrenia
Irina Rish, Benjamin Thyreau, Bertrand Thirion et al.
Distribution Matching for Transduction
Novi Quadrianto, James Petterson, Alex J. Smola
DUOL: A Double Updating Approach for Online Learning
Peilin Zhao, Steven C. Hoi, Rong Jin
Efficient and Accurate Lp-Norm Multiple Kernel Learning
Marius Kloft, Ulf Brefeld, Pavel Laskov et al.