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
A review on machine learning principles for multi-view biological data integration.
TL;DR: It is shown that Bayesian models are able to use prior information and model measurements with various distributions, and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.
Journal ArticleDOI
Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi-Supervised Learning Strategy
TL;DR: This work proposes to represent metamaterials and model the inverse design problem in a probabilistically generative manner, enabling to elegantly investigate the complex structure–performance relationship in an interpretable way, and solve the one‐to‐many mapping issue that is intractable in a deterministic model.
Journal ArticleDOI
Data-efficient and weakly supervised computational pathology on whole-slide images.
Ming Y. Lu,Ming Y. Lu,Ming Y. Lu,Drew F. K. Williamson,Tiffany Y. Chen,Richard J. Chen,Richard J. Chen,Matteo Barbieri,Matteo Barbieri,Faisal Mahmood,Faisal Mahmood,Faisal Mahmood +11 more
TL;DR: In this article, a clustering-constrained-attention multiple-instance learning (CLAM) method is proposed to identify subregions of high diagnostic value to accurately classify whole slides and instance level clustering over the identified representative regions to constrain and refine the feature space.
Journal ArticleDOI
A survey and critique of multiagent deep reinforcement learning
TL;DR: In this paper, the authors provide a clear overview of current multi-agent deep reinforcement learning (MDRL) literature, and provide general guidelines to new practitioners in the area: describing lessons learned from MDRL works, pointing to recent benchmarks, and outlining open avenues of research.
Journal ArticleDOI
Efficient representation of quantum many-body states with deep neural networks
Xun Gao,Luming Duan,Luming Duan +2 more
TL;DR: In this article, the authors present an analysis of the capabilities of recently-proposed neural network representations for storing physically accessible quantum states, including the ground states of many-body Hamiltonians and states generated by quantum dynamics.
References
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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.