Journal ArticleDOI
A fast learning algorithm for deep belief nets
Reads0
Chats0
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
More filters
Journal ArticleDOI
Artificial intelligence: a survey on evolution, models, applications and future trends
TL;DR: Artificial intelligence (AI) is one of the core drivers of industrial development and a critical factor in promoting the integration of emerging technologies, such as graphic processing unit, interconnect and reinforcement learning.
Journal ArticleDOI
A review of Convolutional-Neural-Network-based action recognition
TL;DR: This paper presents a comprehensive review of the CNN-based action recognition methods according to three strategies and provides a guide for future research.
Journal ArticleDOI
A Review on Deep Learning Methods for ECG Arrhythmia Classification
TL;DR: A comprehensive review study on the recent DL methods applied to the ECG signal for the classification purposes, which showed high accuracy in correct classification of Atrial Fibrillation, Supraventricular ECTopic Beats, and Ventricular Ectopic Beats using the GRU, CNN, and LSTM, respectively.
Journal ArticleDOI
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders
TL;DR: This paper first introduces fully convolutional auto-encoders for image feature learning and then proposes a unified clustering framework to learn image representations and cluster centers jointly based on a fully Convolutional Auto-encoder and soft k-means scores.
Journal ArticleDOI
Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding
TL;DR: In this paper, a deep-learning-enabled mmWave massive MIMO framework for effective hybrid precoding is proposed, in which each selection of the precoders for obtaining the optimized decoder is regarded as a mapping relation in the deep neural network (DNN).
References
More filters
Journal ArticleDOI
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.