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
Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Hamido Fujita,Shu Lih Oh,Yuki Hagiwara,Jen Hong Tan,Muhammad Adam +7 more
TL;DR: A convolutional neural network algorithm is implemented for the automated detection of a normal and MI ECG beats (with noise and without noise) and can accurately detect the unknown ECG signals even with noise.
Journal ArticleDOI
Imaging-based molecular barcoding with pixelated dielectric metasurfaces.
Andreas Tittl,Aleksandrs Leitis,Mingkai Liu,Filiz Yesilkoy,Duk-Yong Choi,Dragomir N. Neshev,Yuri S. Kivshar,Hatice Altug +7 more
TL;DR: An imaging-based nanophotonic technique can resolve absorption fingerprints without the need for spectrometry, frequency scanning, or moving mechanical parts, thereby paving the way toward sensitive and versatile miniaturized mid-infrared spectroscopy devices.
Journal ArticleDOI
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network.
TL;DR: In this article, a deep CNN, called Decompose, Transfer, and Compose (DeTraC), was used for the classification of COVID-19 chest X-ray images.
Journal ArticleDOI
DeepDTA: deep drug–target binding affinity prediction
TL;DR: A deep learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities is proposed, outperforming the KronRLS algorithm and SimBoost, a state‐of‐the‐art method for DT binding affinity prediction.
Journal ArticleDOI
DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences
Daniel Quang,Xiaohui Xie +1 more
TL;DR: The DanQ model, a novel hybrid convolutional and bi-directional long short-term memory recurrent neural network framework for predicting non-coding function de novo from sequence, improves considerably upon other models across several metrics.
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.