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
Speech Emotion Recognition Using Deep Learning Techniques: A Review
Ruhul Amin Khalil,Edward Jones,Mohammad Inayatullah Babar,Tariqullah Jan,Mohammad Haseeb Zafar,Thamer Alhussain +5 more
TL;DR: An overview of Deep Learning techniques is presented and some recent literature where these methods are utilized for speech-based emotion recognition is discussed, including databases used, emotions extracted, contributions made toward speech emotion recognition and limitations related to it.
Journal ArticleDOI
Optimal approximation of piecewise smooth functions using deep ReLU neural networks.
TL;DR: It is proved that one cannot approximate a general function f∈Eβ(Rd) using neural networks that are less complex than those produced by the construction, which partly explains the benefits of depth for ReLU networks by showing that deep networks are necessary to achieve efficient approximation of (piecewise) smooth functions.
Journal ArticleDOI
Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms
TL;DR: The results reveal the effectiveness of the proposed method in classifying brain tumor via MRI images and can be readily used in practice for assisting the doctor to diagnose brain tumors in an early stage.
Proceedings ArticleDOI
Dynamic Key-Value Memory Networks for Knowledge Tracing
TL;DR: Li et al. as discussed by the authors introduced a new model called Dynamic Key-Value Memory Networks (DKVMN) that can exploit the relationships between underlying concepts and directly output a student's mastery level of each concept.
Journal ArticleDOI
Hyperspectral Image Classification With Deep Learning Models
TL;DR: This paper advocates four new deep learning models, namely, 2-D convolutional neural network, 3-D-CNN, recurrent 2- D CNN, recurrent R-2-D CNN, and recurrent 3- D-CNN for hyperspectral image classification.
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.