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Thought this was cool: Resources about deeplearning

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This post is not finished. I will add more resources in future….


Restrict Boltzmann Machine

  1. A Practical Guide to Training Restricted Boltzmann Machines This article help you implement the RBM
  2. A Fast Learning Algorithm for Deep Belief Nets How to train deep network by stacked RBM
  3. Biasing Restricted Boltzmann Machines to Manipulate Latent Selectivity and Sparsity How to make RBM sparse so that every hidden unit can represent a simple features.
  4. Sparse deep belief net model for visual area V2 How to training the sparse RBM

RBM in Text Mining

  1. Replicated Softmax: an Undirected Topic Model This paper discuss how to use softmax activation function model multi-nominal distribution
  2. Training Restricted Boltzmann Machines on Word Observations Softmax have high time complexity. This paper discuss how to improve performance of softmax
  3. A Neural Autoregressive Topic Model This paper consider order of words in a document. Given first n – 1 words, predict the nst word. This model’s performance is good

    1. This paper’s idea comes from this page The Neural Autoregressive Distribution Estimator , this paper introduce how to convert RBM to bayesian network

Sparse Coding

The sparse coding algorithm is consist of two steps :

  1. Given basis, learning sparse representation of samples
  2. Given sparse representation of samples, learning basis

The first step is a quadratic optimization problem under L1 regularization. And this step is very time consuming. Following methods have been proposed to solve this problem:

  1. LASSO
  2. LARS
  3. Feature-Sign

Auto Encoder

Auto Encoder is a neural network which try to re-construct the input in the output layer.





[Book] Monte Carlo Statistical Methods



from xlvector – Recommender System:


Written by cwyalpha

五月 12, 2013 在 8:38 上午

发表在 Uncategorized


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