Journal ArticleDOI
Deep learning
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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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Journal ArticleDOI
Applications for Deep Learning in Ecology
TL;DR: It is argued that at a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be effectively processed by humans anymore, deep learning could become a powerful reference tool for ecologists.
Journal ArticleDOI
Advancing Drug Discovery via Artificial Intelligence
TL;DR: Emerging applications of AI to improve the drug discovery process are discussed here to make the hunt for new pharmaceuticals quicker, cheaper, and more effective.
Posted Content
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
TL;DR: This work goes beyond ConvLSTM and proposes the Trajectory GRU (TrajGRU) model that can actively learn the location-variant structure for recurrent connections, and provides a benchmark that includes a real-world large-scale dataset from the Hong Kong Observatory.
Posted Content
Deep Subspace Clustering Networks
TL;DR: In this article, a self-expressive layer between the encoder and the decoder is introduced to mimic the self-expressiveness property that has proven effective in traditional subspace clustering.
Journal ArticleDOI
Application of Artificial Intelligence to Gastroenterology and Hepatology
Catherine Le Berre,William J. Sandborn,Sabeur Aridhi,Marie-Dominique Devignes,Laure Fournier,Malika Smaïl-Tabbone,Silvio Danese,Laurent Peyrin-Biroulet +7 more
TL;DR: The ways in which AI may help physicians make a diagnosis or establish a prognosis are reviewed and its limitations are discussed, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.
References
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Journal ArticleDOI
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.
Journal ArticleDOI
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.