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
More filters
Proceedings ArticleDOI
Learning to Match using Local and Distributed Representations of Text for Web Search
TL;DR: This work proposes a novel document ranking model composed of two separate deep neural networks, one that matches the query and the document using a local representation, and another that Matching with distributed representations complements matching with traditional local representations.
Journal ArticleDOI
Data-analysis strategies for image-based cell profiling
Juan C. Caicedo,Sam Cooper,Florian Heigwer,Scott Warchal,Peng Qiu,Csaba Molnar,Aliaksei Vasilevich,Joseph Barry,Harmanjit Singh Bansal,Oren Kraus,Mathias Wawer,Lassi Paavolainen,Markus D. Herrmann,Mohammad Hossein Rohban,Jane Hung,Jane Hung,Holger Hennig,John Concannon,Ian Smith,Paul A. Clemons,Shantanu Singh,Paul Rees,Paul Rees,Peter Horvath,Peter Horvath,Roger G. Linington,Anne E. Carpenter +26 more
TL;DR: The steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images are introduced and techniques that have proven useful in each stage of the data analysis process are recommended on the basis of the experience of 20 laboratories worldwide that are refining their image- based cell-profiling methodologies.
Journal ArticleDOI
Accelerating the discovery of materials for clean energy in the era of smart automation
Daniel P. Tabor,Loïc M. Roch,Semion K. Saikin,Christoph Kreisbeck,Dennis Sheberla,Joseph Montoya,Shyam Dwaraknath,Muratahan Aykol,Carlos Ortiz,Hermann Tribukait,Carlos Amador-Bedolla,Christoph J. Brabec,Benji Maruyama,Kristin A. Persson,Kristin A. Persson,Alán Aspuru-Guzik +15 more
TL;DR: It is envisioned that a closed-loop approach, which combines high-throughput computation, artificial intelligence and advanced robotics, will sizeably reduce the time to deployment and the costs associated with materials development.
Journal ArticleDOI
A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.
TL;DR: In this paper, the authors employed deep learning algorithms combined with deformable models to develop and evaluate a fully automatic LV segmentation tool from short-axis cardiac MRI datasets, which outperformed the state-of-the-art methods.
Journal ArticleDOI
Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification
TL;DR: A Multi-crop Convolutional Neural Network (MC-CNN) is presented to automatically extract nodule salient information by employing a novel multi-crop pooling strategy which crops different regions from convolutional feature maps and then applies max-pooling different times.
References
More filters
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.