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
Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization
TL;DR: By analyzing the kernels of the convolutional layers of DNCNN via NAM algorithm, it is found that these kernels act as filters and they become complex when the layers go deeper, which may help to understand what DNCNN has learned in intelligent fault diagnosis of machinery.
Journal ArticleDOI
A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images.
TL;DR: A Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs) and was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP andST regions.
Journal ArticleDOI
A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy
Alexander J. E. Kell,Daniel L. K. Yamins,Erica N. Shook,Sam V. Norman-Haignere,Josh H. McDermott,Josh H. McDermott +5 more
TL;DR: A core goal of auditory neuroscience is to build quantitative models that predict cortical responses to natural sounds, and hierarchical neural networks for speech and music recognition were optimized to solve ecologically relevant tasks.
Journal ArticleDOI
Why and When Can Deep – but Not Shallow – Networks Avoid the Curse of Dimensionality: a Review
Tomaso Poggio,Hrushikesh N. Mhaskar,Hrushikesh N. Mhaskar,Lorenzo Rosasco,Brando Miranda,Qianli Liao +5 more
TL;DR: In this article, the authors review and extend an emerging body of theoretical results on deep learning including the conditions under which it can be exponentially better than shallow learning, and a class of deep convolutional networks represent a special case of these conditions, though weight sharing is not the main reason for their exponential advantage.
Journal ArticleDOI
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks
TL;DR: In this article, a Deep Reinforcement Learning-based Online Offloading (DROO) framework is proposed to optimize task offloading decisions and wireless resource allocation to the time-varying wireless channel conditions.
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.
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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.