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(17 Dec 2013, Main.s1361932)
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Representation Learning: A Review and New Perspectives
- (Bengio) Reviews representation learning in probabalistic models (e.g. BMs), reconstruction approaches (e.g. autoencoders), and geometrically motivated manifold-learning approaches (e.g. PCA models a linear manifold)
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Causal Inference on Time Series using Restricted Structural Equation Models
] - Not familiar enough with concepts
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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
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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
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Learning Multi-level Sparse Representations
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Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising
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Understanding Dropout
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On the Expressive Power of Restricted Boltzmann Machines
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Adaptive dropout for training deep neural networks
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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
The Neural Representation Benchmark and its Evaluation on Brain and Machine
Efficient Learning of Domain-invariant Image Representations
Deep Learning of Recursive Structure: Grammar Induction
Feature Learning in Deep Neural Networks - A Study on Speech Recognition Tasks
Joint Training Deep Boltzmann Machines for Classification
What Regularized Auto-Encoders Learn from the Data Generating Distribution
--
AmosStorkey
- 17 Dec 2013
List of papers that
might
have some use, haven't fully read and needs pruning:
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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
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Dropout Training as Adaptive Regularization
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Fast dropout training
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Sparse coding for multitask and transfer learning
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Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations
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Simple Sparsiļ¬cation Improves Sparse Denoising Autoencoders in Denoising Highly Noisy Images
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On A Nonlinear Generalization of Sparse Coding and Dictionary Learning
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Topic revision: r3 - 17 Dec 2013 - 16:04:29 - Main.s1361932
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