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 Learning in Food: A Review.
TL;DR: A brief introduction of deep learning was provided and the structure of some popular architectures of deep neural networks and the approaches for training a model were detailed, indicating that deep learning outperforms other methods such as manual feature extractors, conventional machine learning algorithms, and deep learning as a promising tool in food quality and safety inspection.
Journal ArticleDOI
Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection
TL;DR: A novel two-stage architecture of a neural network was proposed for plant disease classification focused on a real environment and a new dataset containing 79,265 images was introduced with the aim to become the largest dataset containing leaf images.
Journal ArticleDOI
A deep feature based framework for breast masses classification
TL;DR: A deep feature based framework for breast mass classification task that mainly contains a convolutional neural network (CNN) and a decision mechanism to better simulate the diagnostic procedure operated by doctors and achieved state-of-art performance.
Journal ArticleDOI
Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review.
TL;DR: A general analysis of the current status of CAD systems according to the used image modalities and the machine learning based classifiers is provided.
Journal ArticleDOI
Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit
Tiankuang Zhou,Xing Lin,Jiamin Wu,Yitong Chen,Hao Xie,Yipeng Li,Jingtao Fan,Huaqiang Wu,Lu Fang,Qionghai Dai +9 more
TL;DR: This work proposes an optoelectronic reconfigurable computing paradigm by constructing a diffractive processing unit (DPU) that can efficiently support different neural networks and achieve a high model complexity with millions of neurons.
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.