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
Stochastic Configuration Networks: Fundamentals and Algorithms
Dianhui Wang,Ming Li +1 more
TL;DR: In this paper, the authors proposed a stochastic configuration (SCN) algorithm for neural networks, which randomly assigns the input weights and biases of hidden nodes in the light of a supervisory mechanism, and the output weights are analytically evaluated in either a constructive or selective manner.
Journal ArticleDOI
Deep-Learning-Based Drug-Target Interaction Prediction.
TL;DR: To accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, a deep-learning-based algorithmic framework named DeepDTIs is developed that reaches or outperforms other state-of-the-art methods.
Journal ArticleDOI
Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method
TL;DR: Li et al. as mentioned in this paper proposed a 2.5D convolutional neural network (DCNN) for liver lesion segmentation, which takes a stack of adjacent slices as input and produces the segmentation map corresponding to the center slice.
Journal ArticleDOI
Machine learning and deep learning
TL;DR: In this article, the authors summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business.
Journal ArticleDOI
A Perspective on Deep Imaging
TL;DR: The combination of tomographic imaging and deep learning, or machine learning in general, promises to empower not only image analysis but also image reconstruction as discussed by the authors, and the latter aspect is considered in this perspective article with an emphasis on medical imaging to develop a new generation of image reconstruction theories and techniques.
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.
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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.
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.