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
Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex
Jeff Hawkins,Subutai Ahmad +1 more
TL;DR: This paper shows that a neuron with several thousand synapses segregated on active dendrites can recognize hundreds of independent patterns of cellular activity even in the presence of large amounts of noise and pattern variation, and proposes a network model based on neurons with these properties that learns time-based sequences.
Journal ArticleDOI
Computer vision and deep learning–based data anomaly detection method for structural health monitoring:
TL;DR: Inspired by the real-world manual inspection process, a computer vision and deep learning–based data anomaly detection method is proposed that shows that the multi-pattern anomalies of the data can be automatically detected with high accuracy.
Journal ArticleDOI
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
TL;DR: More than 250 major publications are cited in this survey covering different aspects of the research, including benchmark datasets and state-of-the-art results as discussed by the authors, in retrospect of what has been achieved so far and open challenges and directions for future research.
Journal ArticleDOI
Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review.
Titus J. Brinker,Titus J. Brinker,Achim Hekler,Jochen Utikal,Jochen Utikal,Niels Grabe,Dirk Schadendorf,Joachim Klode,Carola Berking,Theresa Steeb,Alexander Enk,Christof von Kalle +11 more
TL;DR: This study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs, and discusses why the comparability of the presented procedures is very difficult and which challenges must be addressed in the future.
Journal ArticleDOI
Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning
TL;DR: This paper transmute OpCodes into a vector space and applies a deep Eigenspace learning approach to classify malicious and benign applications and presents a deep learning based method to detect Internet of Battlefield Things malware via the device’s Operational Code (OpCode) sequence.
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.
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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.