• Corpus ID: 5867279

Sparse Feature Learning for Deep Belief Networks

@inproceedings{Ranzato2007SparseFL,
  title={Sparse Feature Learning for Deep Belief Networks},
  author={Marc'Aurelio Ranzato and Y-Lan Boureau and Yann LeCun},
  booktitle={NIPS},
  year={2007}
}
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the raw input. Many unsupervised methods are based on reconstructing the input from the representation, while constraining the representation to have certain desirable properties (e.g. low dimension, sparsity, etc). Others are based on approximating density by stochastically reconstructing the input from the representation… 

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References

SHOWING 1-10 OF 26 REFERENCES

A Fast Learning Algorithm for Deep Belief Nets

A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.

Greedy Layer-Wise Training of Deep Networks

These experiments confirm the hypothesis that the greedy layer-wise unsupervised training strategy mostly helps the optimization, by initializing weights in a region near a good local minimum, giving rise to internal distributed representations that are high-level abstractions of the input, bringing better generalization.

A Unified Energy-Based Framework for Unsupervised Learning

A view of unsupervised learning is introduced that integrates probabilistic and nonprobabilistic methods for clustering, dimensionality reduction, and feature extraction in a unified framework and shows that a simple solution is to restrict the amount of information contained in codes that represent the data.

Energy-Based Models for Sparse Overcomplete Representations

A new way of extending independent components analysis (ICA) to overcomplete representations that defines features as deterministic (linear) functions of the inputs and assigns energies to the features through the Boltzmann distribution.

Learning Overcomplete Representations

It is shown that overcomplete bases can yield a better approximation of the underlying statistical distribution of the data and can thus lead to greater coding efficiency and provide a method for Bayesian reconstruction of signals in the presence of noise and for blind source separation when there are more sources than mixtures.

Scaling learning algorithms towards AI

It is argued that deep architectures have the potential to generalize in non-local ways, i.e., beyond immediate neighbors, and that this is crucial in order to make progress on the kind of complex tasks required for artificial intelligence.

Learning Sparse Overcomplete Codes for Images

A survey of algorithms that perform dictionary learning and sparse coding is presented and a modified version of the FOCUSS algorithm is presented that can find a non-negative sparse coding in some cases.

Learning the parts of objects by non-negative matrix factorization

An algorithm for non-negative matrix factorization is demonstrated that is able to learn parts of faces and semantic features of text and is in contrast to other methods that learn holistic, not parts-based, representations.

Reducing the Dimensionality of Data with Neural Networks

This work describes an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.

Learning Sparse Multiscale Image Representations

A method for learning sparse multiscale image representations using a sparse prior distribution over the basis function coefficients, which includes a mixture of a Gaussian and a Dirac delta function, and thus encourages coefficients to have exact zero values.