Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]

@article{Arel2010DeepML,
  title={Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]},
  author={Itamar Arel and Derek C. Rose and Thomas P. Karnowski},
  journal={IEEE Computational Intelligence Magazine},
  year={2010},
  volume={5},
  pages={13-18}
}
This article provides an overview of the mainstream deep learning approaches and research directions proposed over the past decade. It is important to emphasize that each approach has strengths and "weaknesses, depending on the application and context in "which it is being used. Thus, this article presents a summary on the current state of the deep machine learning field and some perspective into how it may evolve. Convolutional Neural Networks (CNNs) and Deep Belief Networks (DBNs) (and their… 

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References

SHOWING 1-10 OF 57 REFERENCES

An empirical evaluation of deep architectures on problems with many factors of variation

TLDR
A series of experiments indicate that these models with deep architectures show promise in solving harder learning problems that exhibit many factors of variation.

Learning Deep Architectures for AI

TLDR
The motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer modelssuch as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks are discussed.

DeSTIN: A Scalable Deep Learning Architecture with Application to High-Dimensional Robust Pattern Recognition

TLDR
A Deep SpatioTemporal Inference Network (DeSTIN) is presented — a scalable deep learning architecture that relies on a combination of unsupervised learning and Bayesian inference, particularly in the context of high-dimensional signal classification.

A new class of convolutional neural networks (SICoNNets) and their application of face detection

  • F. TiviveA. Bouzerdoum
  • Computer Science
    Proceedings of the International Joint Conference on Neural Networks, 2003.
  • 2003
TLDR
A new class of convolutional neural networks, namely shunting inhibitory convolutionAL neural networks (SICoNNets), is introduced, and a training algorithm is developed using supervised learning based on resilient backpropagation with momentum.

Unsupervised feature learning for audio classification using convolutional deep belief networks

In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. However, to our knowledge, these deep learning

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

TLDR
The convolutional deep belief network is presented, a hierarchical generative model which scales to realistic image sizes and is translation-invariant and supports efficient bottom-up and top-down probabilistic inference.

Greedy Layer-Wise Training of Deep Networks

TLDR
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 Fast Learning Algorithm for Deep Belief Nets

TLDR
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.

Best practices for convolutional neural networks applied to visual document analysis

TLDR
A set of concrete bestpractices that document analysis researchers can use to get good results with neural networks, including a simple "do-it-yourself" implementation of convolution with a flexible architecture suitable for many visual document problems.

Deep learning from temporal coherence in video

TLDR
A learning method for deep architectures that takes advantage of sequential data, in particular from the temporal coherence that naturally exists in unlabeled video recordings, and is used to improve the performance on a supervised task of interest.
...