Journal ArticleDOI
Deep learning
TLDR
Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.Abstract:
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.read more
Citations
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Medical image retrieval using deep convolutional neural network
TL;DR: This paper proposes a framework of deep learning for CBMIR system by using deep convolutional neural network (CNN) that is trained for classification of medical images that is best suited to retrieve multimodal medical images for different body organs.
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Uniting the Tribes: Using Text for Marketing Insight
TL;DR: The authors found that words are part of almost every marketplace interaction, including online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data.
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A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders
TL;DR: The results confirm that the proposed method can get rid of the dependence on manual feature extraction and overcome the limitations of individual deep learning models, which is more effective than the existing intelligent diagnosis methods.
Convolutional neural network architectures for predicting DNA–protein binding
TL;DR: A systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets is presented, finding that adding convolutional kernels to a network is important for motif-based tasks and creating a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures.
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A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities
Andrea Capponi,Claudio Fiandrino,Burak Kantarci,Luca Foschini,Dzmitry Kliazovich,Pascal Bouvry +5 more
TL;DR: A survey on existing works in the MCS domain is presented and a detailed taxonomy is proposed to shed light on the current landscape and classify applications, methodologies, and architectures to outline potential future research directions and synergies with other research areas.
References
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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.
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Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Journal ArticleDOI
Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, 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 is described.