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
Stable Architectures for Deep Neural Networks
Eldad Haber,Lars Ruthotto +1 more
TL;DR: New forward propagation techniques inspired by systems of Ordinary Differential Equations (ODE) are proposed that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks.
Journal ArticleDOI
VAMPnets for deep learning of molecular kinetics.
TL;DR: A deep learning framework that automates construction of Markov state models from MD simulation data is introduced that performs equally or better than state-of-the-art Markov modeling methods and provides easily interpretable few-state kinetic models.
Journal ArticleDOI
Solving inverse problems using data-driven models
TL;DR: This survey paper aims to give an account of some of the main contributions in data-driven inverse problems.
Journal ArticleDOI
On the use of deep learning for computational imaging
TL;DR: This paper relates the deep-learning-inspired solutions to the original computational imaging formulation and use the relationship to derive design insights, principles, and caveats of more general applicability, and explores how the machine learning process is aided by the physics of imaging when ill posedness and uncertainties become particularly severe.
Journal ArticleDOI
Fully Automated Echocardiogram Interpretation in Clinical Practice
Jeffrey Zhang,Sravani Gajjala,Pulkit Agrawal,Geoffrey H. Tison,Laura A. Hallock,Lauren Beussink-Nelson,Mats Christian Højbjerg Lassen,Eugene Fan,Mandar A. Aras,Cha Randle Jordan,Kirsten E. Fleischmann,Michelle E. Melisko,Atif Qasim,Sanjiv J. Shah,Ruzena Bajcsy,Rahul C. Deo +15 more
TL;DR: In this paper, the authors proposed automated cardiac image interpretation has the potential to transform clinical practice in multiple ways, including enabling serial assessment of cardiac function by nonexperts in prima...
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.
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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.
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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.