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
State-of-the-art in artificial neural network applications: A survey
Oludare Isaac Abiodun,Oludare Isaac Abiodun,Aman Jantan,Abiodun Esther Omolara,Kemi Victoria Dada,Nachaat AbdElatif Mohamed,Humaira Arshad +6 more
TL;DR: The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems and proposed feedforwardand feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance.
Journal ArticleDOI
CellProfiler 3.0: Next-generation image processing for biology.
Claire McQuin,Allen Goodman,Vasiliy S. Chernyshev,Vasiliy S. Chernyshev,Lee Kamentsky,Beth A. Cimini,Kyle W. Karhohs,Minh Doan,Liya Ding,Susanne M. Rafelski,Derek Thirstrup,Winfried Wiegraebe,Shantanu Singh,Tim Becker,Juan C. Caicedo,Anne E. Carpenter +15 more
TL;DR: CellProfiler 3.0 is described, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional image stacks, increasingly common in biomedical research.
Proceedings Article
Dueling network architectures for deep reinforcement learning
TL;DR: In this paper, a dueling network is proposed to represent two separate estimators for the state value function and the state-dependent advantage function, which leads to better policy evaluation in the presence of many similar-valued actions.
Journal ArticleDOI
Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique
TL;DR: The papers in this special section focus on the technology and applications supported by deep learning, which have proven to be powerful tools for a broad range of computer vision tasks.
Posted Content
Deep Convolutional Networks on Graph-Structured Data.
TL;DR: This paper develops an extension of Spectral Networks which incorporates a Graph Estimation procedure, that is test on large-scale classification problems, matching or improving over Dropout Networks with far less parameters to estimate.
References
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Long short-term memory
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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.