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
Cnvlutin: ineffectual-neuron-free deep neural network computing
Jorge Albericio,Patrick Judd,Tayler Hetherington,Tor M. Aamodt,Natalie Enright Jerger,Andreas Moshovos +5 more
TL;DR: Cnvolutin (CNV), a value-based approach to hardware acceleration that eliminates most of these ineffectual operations, improving performance and energy over a state-of-the-art accelerator with no accuracy loss.
Journal ArticleDOI
Deep learning: new computational modelling techniques for genomics
TL;DR: This Review describes different deep learning techniques and how they can be applied to extract biologically relevant information from large, complex genomic data sets.
Journal ArticleDOI
Phase recovery and holographic image reconstruction using deep learning in neural networks
TL;DR: It is demonstrated that a neural network can learn to perform phase recovery and holographic image reconstruction after appropriate training, and this deep learning-based approach provides an entirely new framework to conduct holographic imaging by rapidly eliminating twin-image and self-interference-related spatial artifacts.
Journal ArticleDOI
Deep learning for universal linear embeddings of nonlinear dynamics.
TL;DR: It is often advantageous to transform a strongly nonlinear system into a linear one in order to simplify its analysis for prediction and control, so the authors combine dynamical systems with deep learning to identify these hard-to-find transformations.
Journal ArticleDOI
Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Kaustav Bera,Kurt A. Schalper,David L. Rimm,Vamsidhar Velcheti,Anant Madabhushi,Anant Madabhushi +5 more
TL;DR: A broad framework is provided for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development, and some of the challenges relating to the use of AI are discussed, including the need for well-curated validation datasets, regulatory approval and fair reimbursement strategies.
References
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Long short-term memory
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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.