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
A Proposal on Machine Learning via Dynamical Systems
TL;DR: The idea of using continuous dynamical systems to model general high-dimensional nonlinear functions used in machine learning and the connection with deep learning is discussed.
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: In this article, the authors consider key factors contributing to this issue, explore how federated learning may provide a solution for the future of digital health and highlight the challenges and considerations that need to be addressed.
Journal ArticleDOI
Low-dose CT via convolutional neural network
TL;DR: A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion, demonstrating a great potential of the proposed method on artifact reduction and structure preservation.
Journal ArticleDOI
Toward Causal Representation Learning
Bernhard Schölkopf,Francesco Locatello,Stefan Bauer,Nan Rosemary Ke,Nal Kalchbrenner,Anirudh Goyal,Yoshua Bengio +6 more
TL;DR: The authors reviewed fundamental concepts of causal inference and related them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research.
Journal ArticleDOI
Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges
TL;DR: The focus of this review is to provide in-depth summaries of deep learning methods for mobile and wearable sensor-based human activity recognition, and categorise the studies into generative, discriminative and hybrid methods.
References
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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.