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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Journal ArticleDOI
Metaheuristic design of feedforward neural networks
TL;DR: A broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches are summarized, which provides interesting research challenges for future research to cope-up with the present information processing era.
Book ChapterDOI
Predictive Business Process Monitoring with LSTM Neural Networks
TL;DR: In this paper, Long Short-Term Memory (LSTM) neural networks are used to predict the timestamp of the next event of a running case and the remaining time of the running case.
Journal ArticleDOI
Federated Learning for Healthcare Informatics
TL;DR: In this article, the authors provide a review of federated learning in the biomedical space, and summarize the general solutions to the statistical challenges, system challenges, and privacy issues in federated Learning, and point out the implications and potentials in healthcare.
Journal ArticleDOI
Inference in artificial intelligence with deep optics and photonics.
Gordon Wetzstein,Aydogan Ozcan,Sylvain Gigan,Shanhui Fan,Dirk Englund,Marin Soljacic,Cornelia Denz,David A. B. Miller,Demetri Psaltis +8 more
TL;DR: Recent work on optical computing for artificial intelligence applications is reviewed and its promise and challenges are discussed.
Journal ArticleDOI
Deep learning in biomedicine.
TL;DR: This work argues that challenges in guaranteeing the performance of deployed systems and in establishing trust with stakeholders, clinicians and regulators will be overcome using the same flexibility that created them; for example, by training deep models so that they can output a rationale for their predictions.
References
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Long short-term memory
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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.