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
Prototype-Based Discriminative Feature Learning for Kinship Verification
TL;DR: Experimental results on four publicly available kinship datasets show the superior performance of the proposed PDFL methods over both the state-of-the-art kinship verification methods and human ability in the kinships verification task.
Proceedings ArticleDOI
Convolutional Neural Networks-based continuous speech recognition using raw speech signal
TL;DR: The studies show that the CNN-based approach achieves better performance than the conventional ANN- based approach with as many parameters and that the features learned from raw speech by the CNN -based approach could generalize across different databases.
Journal ArticleDOI
Neighborhood Discriminant Hashing for Large-Scale Image Retrieval
TL;DR: This paper proposes a novel hashing method named neighborhood discriminant hashing (NDH) (for short) to implement approximate similarity search by exploiting local discriminative information, i.e., the labels of a sample can be inherited from the neighbor samples it selects.
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Dissolved gas analysis of insulating oil for power transformer fault diagnosis with deep belief network
TL;DR: The proposed method significantly improves the accuracy of power transformer fault diagnosis by analyzing the relationship between the gases dissolved in transformer oil and fault types and the Noncode ratios of the gases are determined as the characterizing parameter of the DBN model.
Journal ArticleDOI
Development of a deep residual learning algorithm to screen for glaucoma from fundus photography
Naoto Shibata,Masaki Tanito,Keita Mitsuhashi,Yuri Fujino,Yuri Fujino,Masato Matsuura,Masato Matsuura,Hiroshi Murata,Ryo Asaoka +8 more
TL;DR: A deep residual learning algorithm was developed to screen for glaucoma from fundus photography and measure its diagnostic performance compared to Residents in Ophthalmology, and its diagnostic accuracy was validated in the testing dataset, using the area under the receiver operating characteristic curve (AROC).
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.
Book
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.