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
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence
TL;DR: It was shown that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams and provided an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain.
Proceedings ArticleDOI
SemEval-2018 Task 1: Affect in Tweets
TL;DR: This work presents the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet, with a focus on the techniques and resources that are particularly useful.
Journal ArticleDOI
Toward a Rational and Mechanistic Account of Mental Effort.
Amitai Shenhav,Sebastian Musslick,Falk Lieder,Wouter Kool,Thomas L. Griffiths,Jonathan D. Cohen,Matthew Botvinick +6 more
TL;DR: This review explores recent advances in computational modeling and empirical research aimed at addressing questions at the level of psychological process and neural mechanism, examining both the limitations to mental effort exertion and how the authors manage those limited cognitive resources.
Journal ArticleDOI
Machine learning in chemoinformatics and drug discovery.
TL;DR: Basic principles and recent case studies are presented to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and limitations and future directions are discussed to guide further development in this evolving field.
Journal ArticleDOI
Identification of rice diseases using deep convolutional neural networks
TL;DR: A novel rice diseases identification method based on deep convolutional neural networks (CNNs) techniques, trained to identify 10 common rice diseases with much higher accuracy than conventional machine learning model.
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.
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.