Papers
184,605 papers found
Distribution Matching for Transduction
Novi Quadrianto, James Petterson, Alex J. Smola
Dlib-ml: A Machine Learning Toolkit
Davis E. King
DL-Learner: Learning Concepts in Description Logics
Jens Lehmann
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.
Efficient Bregman Range Search
Lawrence Cayton
Efficient, guaranteed search with multi-agent teams
G. Hollinger, S. Singh and A. Kehagias
Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models
Ryan Mcdonald, Mehryar Mohri, Nathan Silberman et al.
Efficient Learning using Forward-Backward Splitting
Yoram Singer, John C. Duchi
Efficient Match Kernel between Sets of Features for Visual Recognition
Liefeng Bo, Cristian Sminchisescu
Efficient Moments-based Permutation Tests
Chunxiao Zhou, Huixia J. Wang, Yongmei M. Wang
Efficient Online and Batch Learning Using Forward Backward Splitting
John Duchi, Yoram Singer
Efficient Recovery of Jointly Sparse Vectors
Liang Sun, Jun Liu, Jianhui Chen et al.
Ensemble Nystrom Method
Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar
Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification
Amarnag Subramanya, Jeff A. Bilmes
Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks
Jean Hausser, Korbinian Strimmer
Estimating image bases for visual image reconstruction from human brain activity
Yusuke Fujiwara, Yoichi Miyawaki, Yukiyasu Kamitani
Estimating Labels from Label Proportions
Novi Quadrianto, Alex J. Smola, Tibério S. Caetano et al.
Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods
Holger Höfling, Robert Tibshirani
Evaluating multi-class learning strategies in a generative hierarchical framework for object detection
Sanja Fidler, Marko Boben, Ales Leonardis
Evolutionary Model Type Selection for Global Surrogate Modeling
Dirk Gorissen, Tom Dhaene, Filip De Turck
Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model
Ed Vul, George Alvarez, Joshua B. Tenenbaum et al.