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
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images
TL;DR: An auto‐context version of the VoxResNet is proposed by combining the low‐level image appearance features, implicit shape information, and high‐level context together for further improving the segmentation performance, and achieved the best performance in the 2013 MICCAI MRBrainS challenge.
Journal ArticleDOI
A deep learning framework for neuroscience
Blake A. Richards,Timothy P. Lillicrap,Philippe Beaudoin,Yoshua Bengio,Yoshua Bengio,Rafal Bogacz,Amelia J. Christensen,Claudia Clopath,Rui Ponte Costa,Rui Ponte Costa,Archy O. de Berker,Surya Ganguli,Surya Ganguli,Colleen J Gillon,Danijar Hafner,Danijar Hafner,Adam Kepecs,Nikolaus Kriegeskorte,Peter E. Latham,Grace W. Lindsay,Kenneth D. Miller,Richard Naud,Christopher C. Pack,Panayiota Poirazi,Pieter R. Roelfsema,João Sacramento,Andrew M. Saxe,Benjamin Scellier,Anna C. Schapiro,Walter Senn,Greg Wayne,Daniel L. K. Yamins,Friedemann Zenke,Friedemann Zenke,Joel Zylberberg,Joel Zylberberg,Denis Therien,Konrad P. Kording,Konrad P. Kording +38 more
TL;DR: It is argued that a deep network is best understood in terms of components used to design it—objective functions, architecture and learning rules—rather than unit-by-unit computation.
Proceedings ArticleDOI
STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation
TL;DR: It is argued that a long-term memory model may be insufficient for modeling long sessions that usually contain user interests drift caused by unintended clicks, and a novel short-term attention/memory priority model is proposed as a remedy, which is capable of capturing users' general interests from the long- Term memory of a session context, whilst taking into account users' current interest from the short- term memory of the last-clicks.
Journal ArticleDOI
Deep learning in environmental remote sensing: Achievements and challenges
Qiangqiang Yuan,Huanfeng Shen,Tongwen Li,Zhiwei Li,Shuwen Li,Yun Jiang,Hongzhang Xu,Weiwei Tan,Qianqian Yang,Jiwen Wang,Jianhao Gao,Liangpei Zhang +11 more
TL;DR: The potential of DL in environmental remote sensing, including land cover mapping, environmental parameter retrieval, data fusion and downscaling, and information reconstruction and prediction, will be analyzed and a typical network structure will be introduced.
Proceedings ArticleDOI
Deep & Cross Network for Ad Click Predictions
TL;DR: This paper proposes the Deep & Cross Network (DCN), which keeps the benefits of a DNN model, and beyond that, it introduces a novel cross network that is more efficient in learning certain bounded-degree feature interactions.
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.
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.