* Causal Inference on Time Series using Restricted Structural Equation Models] - Not familiar enough with concepts
* What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach - Presents a model that learns features and masks of image patches to model occlusion
* Multi-Prediction Deep Boltzmann Machines - (Bengio) Frank paper that introduces a method of training DBMs in one sweep as apposed to layerwise that does not reduce performance and provides competitive classification ability
* Learning Multi-level Sparse Representations
* Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising
* On the Expressive Power of Restricted Boltzmann Machines
* Adaptive dropout for training deep neural networks
* Deep Generative Stochastic Networks Trainable by Backprop
Also, lets just pick a few talks from
International Conference on Learning Representations 2013
At a glance, these may be intersting
-- AmosStorkey - 17 Dec 2013
List of papers that might have some use, haven't fully read and needs pruning:
* Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs - (Bengio) Applied to text classification, attempts to keep RBM computational training cost linear in the number of non-zeros
* Dropout Training as Adaptive Regularization
* Sparse coding for multitask and transfer learning
* Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations
* Simple Sparsiļ¬cation Improves Sparse Denoising Autoencoders in Denoising Highly Noisy Images
* On A Nonlinear Generalization of Sparse Coding and Dictionary Learning