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
More filters
Journal ArticleDOI
Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer.
Anton S. Becker,Magda Marcon,Soleen Ghafoor,Moritz C. Wurnig,Thomas Frauenfelder,Andreas Boss +5 more
TL;DR: In this paper, the authors evaluated the diagnostic accuracy of a multipurpose image analysis software based on deep learning with artificial neural networks for the detection of breast cancer in an independent, dual-center mammography data set.
Journal ArticleDOI
Distributed deep learning networks among institutions for medical imaging.
Ken Chang,Niranjan Balachandar,Carson Lam,Darvin Yi,James M. Brown,Andrew Beers,Bruce R. Rosen,Daniel L. Rubin,Jayashree Kalpathy-Cramer +8 more
TL;DR: It is shown that distributing deep learning models is an effective alternative to sharing patient data, and this finding has implications for any collaborative deep learning study.
Journal ArticleDOI
A scalable deep learning platform for identifying geologic features from seismic attributes
TL;DR: A “big data” platform to facilitate the work of geophysicists in interpreting and analyzing large volumes of seismic data with scalable performance on a scalable, distributed computing platform.
Journal ArticleDOI
Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches
Mu Zhou,Jacob G. Scott,Baishali Chaudhury,Lawrence O. Hall,Dmitry B. Goldgof,Kristen W. Yeom,Yangming Ou,Jayashree Kalpathy-Cramer,Sandy Napel,Robert J. Gillies,Olivier Gevaert,Robert A. Gatenby +11 more
TL;DR: A cross-disciplinary approach is used to highlight studies in radiomics through computational models that provide novel clinical insights and outline current quantitative image feature extraction and prediction strategies with different levels of available clinical classes for supporting clinical decision-making.
Journal ArticleDOI
Learning data-driven discretizations for partial differential equations.
TL;DR: In this paper, a method for learning optimized approximations to PDEs based on actual solutions to the known underlying equations is proposed, using neural networks to estimate spatial derivatives, which are optimized end to end to best satisfy the equations on a low-resolution grid.
References
More filters
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.