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
Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model.
TL;DR: The structured deep learning model used in this study has achieved remarkable performance on a large-scale dataset, which demonstrates the strength of the method in providing an efficient tool for breast cancer multi-classification in clinical settings.
Journal ArticleDOI
Group sparse regularization for deep neural networks
TL;DR: The group Lasso penalty is extended, originally proposed in the linear regression literature, to impose group-level sparsity on the networks connections, where each group is defined as the set of outgoing weights from a unit.
Journal ArticleDOI
Deep MRI brain extraction: A 3D convolutional neural network for skull stripping
Jens Kleesiek,Gregor Urban,Alexander Hubert,Daniel Schwarz,Klaus H. Maier-Hein,Martin Bendszus,Armin Biller +6 more
TL;DR: A 3D convolutional deep learning architecture to address shortcomings of existing methods, not limited to non-enhanced T1w images, and may prove useful for large-scale studies and clinical trials.
Journal ArticleDOI
Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing.
Elliot J. Fuller,Scott T. Keene,Armantas Melianas,Zhongrui Wang,Sapan Agarwal,Yiyang Li,Yaakov Tuchman,Conrad D. James,Matthew J. Marinella,Jianhua Yang,Alberto Salleo,A. Alec Talin +11 more
TL;DR: An ionic floating-gate memory array based on a polymer redox transistor connected to a conductive-bridge memory (CBM) is introduced, enabling linear and symmetric weight updates in parallel over an entire crossbar array at megahertz rates over 109 write-read cycles.
Posted Content
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
TL;DR: A comprehensive survey of the recent research efforts on EI is conducted, which provides an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge.
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.
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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.