Journal ArticleDOI
Deep learning
Reads0
Chats0
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
Journal ArticleDOI
DLAU: A Scalable Deep Learning Accelerator Unit on FPGA
TL;DR: This paper designs deep learning accelerator unit (DLAU), which is a scalable accelerator architecture for large-scale deep learning networks using field-programmable gate array (FPGA) as the hardware prototype and employs three pipelined processing units to improve the throughput.
Journal ArticleDOI
Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning
Amy McGovern,Ryan Lagerquist,David John Gagne,G. Eli Jergensen,Kimberly L. Elmore,Cameron R. Homeyer,Travis M. Smith +6 more
TL;DR: In this paper, the authors synthesize multiple methods for machine learning (ML) model interpretation and visualization (MIV) focusing on meteorological applications, which has recently exploded in popularity.
Journal ArticleDOI
ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition.
Dipendra Jha,Logan Ward,Arindam Paul,Wei-keng Liao,Alok Choudhary,Chris Wolverton,Ankit Agrawal +6 more
TL;DR: The design and implementation of a deep neural network model referred to as ElemNet is presented; it automatically captures the physical and chemical interactions and similarities between different elements using artificial intelligence which allows it to predict the materials properties with better accuracy and speed.
Journal ArticleDOI
Measuring human perceptions of a large-scale urban region using machine learning
Fan Zhang,Fan Zhang,Fan Zhang,Bolei Zhou,Liu Liu,Yu Liu,Helene H. Fung,Hui Lin,Hui Lin,Carlo Ratti +9 more
TL;DR: A deep learning model, which has been trained on millions of human ratings of street-level imagery, was used to predict human perceptions of a street view image and can help to map the distribution of the city-wide human perception for a new urban region.
Proceedings ArticleDOI
Disease detection on the leaves of the tomato plants by using deep learning
TL;DR: The aim of this work is to detect diseases that occur on plants in tomato fields or in their greenhouses by using deep learning to detect the various diseases on the leaves of tomato plants.
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.