Journal ArticleDOI
A fast learning algorithm for deep belief nets
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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.Abstract:
We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind.read more
Citations
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Journal ArticleDOI
Scene Recognition by Manifold Regularized Deep Learning Architecture
TL;DR: By the proposed approach, a deep architecture could be designed to learn the high-level features for scene recognition in an unsupervised fashion, and Experiments on standard data sets show that the method outperforms the state-of-the-art used forscene recognition.
Journal ArticleDOI
An overview of state-of-the-art partial discharge analysis techniques for condition monitoring
TL;DR: In this article, a focus of condition monitoring is to detect partial discharge (PD) especially in the early stages to prevent a serious power failure or outage, which is a key indicator of such electrical failure.
Proceedings ArticleDOI
Toward an Online Anomaly Intrusion Detection System Based on Deep Learning
Khaled Alrawashdeh,Carla Purdy +1 more
TL;DR: This work implements a deep learning approach for anomaly detection using a Restricted Boltzmann Machine (RBM) and a deep belief network that outperforms previous deep learning methods implemented by Li and Salama in both detection speed and accuracy.
Book ChapterDOI
Manifold Learning of Brain MRIs by Deep Learning
Tom Brosch,Roger Tam +1 more
TL;DR: A novel method for learning the manifold of 3D brain images that, unlike most existing manifold learning methods, does not require the manifold space to be locally linear, and does not requirement a predefined similarity measure or a prebuilt proximity graph.
Journal ArticleDOI
Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends
Zhen-Hua Ling,Shiyin Kang,Heiga Zen,Andrew W. Senior,Mike Schuster,Xiaojun Qian,Helen Meng,Li Deng +7 more
TL;DR: In this article, Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs) are used for generating low-level speech waveforms from high-level symbolic inputs via intermediate acoustic feature sequences.
References
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Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Journal ArticleDOI
Shape matching and object recognition using shape contexts
TL;DR: This paper presents work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation, and proposes shape detection using a feature called shape context, which is descriptive of the shape of the object.
Journal ArticleDOI
Training products of experts by minimizing contrastive divergence
TL;DR: A product of experts (PoE) is an interesting candidate for a perceptual system in which rapid inference is vital and generation is unnecessary because it is hard even to approximate the derivatives of the renormalization term in the combination rule.
Proceedings ArticleDOI
Best practices for convolutional neural networks applied to visual document analysis
TL;DR: 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.