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
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
TL;DR: This work develops, for the first time, universal MatErials Graph Network (MEGNet) models for accurate property prediction in both molecules and crystals and demonstrates the transfer learning of elemental embeddings from a property model trained on a larger data set to accelerate the training of property models with smaller amounts of data.
Journal ArticleDOI
Applications of structural equation modeling (SEM) in ecological studies: an updated review
TL;DR: The essential components and variants of structural equation modeling (SEM) are introduced, the common issues in SEM applications are synthesized, and the views on SEM’s future in ecological research are shared.
Book ChapterDOI
Adversarial examples for malware detection
TL;DR: This paper presents adversarial examples derived from regular inputs by introducing minor—yet carefully selected—perturbations into machine learning models, showing their robustness against inputs crafted by an adversary.
Journal ArticleDOI
A primer on deep learning in genomics.
James Zou,Mikael Huss,Abubakar Abid,Pejman Mohammadi,Ali Torkamani,Ali Torkamani,Amalio Telenti,Amalio Telenti +7 more
TL;DR: A perspective and primer on deep learning applications for genome analysis and successful applications in the fields of regulatory genomics, variant calling and pathogenicity scores are provided.
Journal ArticleDOI
The Future of Digital Health with Federated Learning
Nicola Rieke,Nicola Rieke,Jonny Hancox,Wenqi Li,Fausto Milletari,Holger R. Roth,Shadi Albarqouni,Shadi Albarqouni,Spyridon Bakas,Mathieu N. Galtier,Bennett A. Landman,Klaus H. Maier-Hein,Klaus H. Maier-Hein,Sebastien Ourselin,Micah J. Sheller,Ronald M. Summers,Andrew Trask,Daguang Xu,Maximilian Baust,M. Jorge Cardoso +19 more
TL;DR: This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.
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.