Papers
176,624 papers found
Towards Understanding Situated Natural Language
Antoine Bordes, Nicolas Usunier, Ronan Collobert et al.
Trading off Mistakes and Don't-Know Predictions
Amin Sayedi, Morteza Zadimoghaddam, Avrim Blum
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM
Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang et al.
Transduction with Matrix Completion: Three Birds with One Stone
Andrew Goldberg, Ben Recht, Junming Xu et al.
Tree Decomposition for Large-Scale SVM Problems
Fu Chang, Chien-Yang Guo, Xiao-Rong Lin et al.
Tree-Structured Stick Breaking for Hierarchical Data
Zoubin Ghahramani, Michael I. Jordan, Ryan P. Adams
Two-Layer Generalization Analysis for Ranking Using Rademacher Average
Wei Chen, Tie-yan Liu, Zhi-ming Ma
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot, Yoshua Bengio
Universal Consistency of Multi-Class Support Vector Classification
Tobias Glasmachers
Universal Kernels on Non-Standard Input Spaces
Andreas Christmann, Ingo Steinwart
Unsupervised Aggregation for Classification Problems with Large Numbers of Categories
Ivan Titov, Alexandre Klementiev, Kevin Small et al.
Unsupervised Kernel Dimension Reduction
Meihong Wang, Fei Sha, Michael I. Jordan
Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels
Pinar Donmez, Guy Lebanon, Krishnakumar Balasubramanian
Using body-anchored priors for identifying actions in single images
Leonid Karlinsky, Michael Dinerstein, Shimon Ullman
Using Contextual Representations to Efficiently Learn Context-Free Languages
Alexander Clark, Rémi Eyraud, Amaury Habrard
Variable Impedance Control - A Reinforcement Learning Approach
J. Buchli, E. Theodorou, F. Stulp and S. Schaal
Variable margin losses for classifier design
Hamed Masnadi-shirazi, Nuno Vasconcelos
Variational bounds for mixed-data factor analysis
Mohammad Emtiyaz Khan, Guillaume Bouchard, Kevin P. Murphy et al.
Variational Inference over Combinatorial Spaces
Alexandre Bouchard-côté, Michael I. Jordan
Variational methods for Reinforcement Learning
Thomas Furmston, David Barber
Variational Relevance Vector Machine for Tabular Data
Dmitry Kropotov, Dmitry Vetrov, Lior Wolf et al.
WEKA−Experiences with a Java Open-Source Project
Remco R. Bouckaert, Eibe Frank, Mark A. Hall et al.
Why are DBNs sparse?
Shaunak Chatterjee, Stuart Russell