Papers
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot, Yoshua Bengio
Variational methods for Reinforcement Learning
Thomas Furmston, David Barber
Posterior distributions are computable from predictive distributions
Cameron Freer, Daniel Roy
A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation
Florence Forbes, Senan Doyle, Daniel Garcia–Lorenzo et al.
Model-Free Monte Carlo-like Policy Evaluation
Raphael Fonteneau, Susan Murphy, Louis Wehenkel et al.
Semi-Supervised Learning via Generalized Maximum Entropy
Ayse Erkan, Yasemin Altun
Why Does Unsupervised Pre-training Help Deep Learning?
Dumitru Erhan, Aaron Courville, Yoshua Bengio et al.
Combining Experiments to Discover Linear Cyclic Models with Latent Variables
Frederick Eberhardt, Patrik Hoyer, Richard Scheines
Neural conditional random fields
Trinh–Minh–Tri Do, Thierry Artieres
Nonparametric Bayesian Matrix Factorization by Power-EP
Nan Ding, Yuan Qi, Rongjing Xiang et al.
Bayesian variable order Markov models
Christos Dimitrakakis
Feature Selection using Multiple Streams
Paramveer Dhillon, Dean Foster, Lyle Ungar
Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines
Guillaume Desjardins, Aaron Courville, Yoshua Bengio et al.
Multiclass-Multilabel Classification with More Classes than Examples
Ofer Dekel, Ohad Shamir
Impossibility Theorems for Domain Adaptation
Shai Ben David, Tyler Lu, Teresa Luu et al.
Improving posterior marginal approximations in latent Gaussian models
Botond Cseke, Tom Heskes
On the Impact of Kernel Approximation on Learning Accuracy
Corinna Cortes, Mehryar Mohri, Ameet Talwalkar
Parametric Herding
Yutian Chen, Max Welling
Focused Belief Propagation for Query-Specific Inference
Anton Chechetka, Carlos Guestrin
Why are DBNs sparse?
Shaunak Chatterjee, Stuart Russell
Towards Understanding Situated Natural Language
Antoine Bordes, Nicolas Usunier, Ronan Collobert et al.
Kernel Partial Least Squares is Universally Consistent
Gilles Blanchard, Nicole Krämer