Results 11 to 20 of about 595,263 (265)
Nature, 2015 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.
Yann LeCun +2 more
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SIAM Journal on Mathematics of Data Science, 2021 Implicit deep learning prediction rules generalize the recursive rules of feedforward neural networks. Such rules are based on the solution of a fixed-point equation involving a single vector of hidden features, which is thus only implicitly defined.
Laurent El Ghaoui +4 more
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CoRR, 2018 arXiv admin note: text overlap with arXiv:1602 ...
Nicholas G. Polson, Vadim O. Sokolov
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DL2: A Deep Learning-Driven Scheduler for Deep Learning Clusters [PDF]
IEEE Transactions on Parallel and Distributed Systems, 2021 More and more companies have deployed machine learning (ML) clusters, where deep learning (DL) models are trained for providing various AI-driven services. Efficient resource scheduling is essential for maximal utilization of expensive DL clusters.
Yanghua Peng +5 more
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Machine Learning, 2023 AbstractThis paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performance instability from different train-validation splits. The proposed framework holistically improves accuracy, robustness, sparsity, and stability over standard deep ...
Dimitris Bertsimas +5 more
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Deep learning in bioinformatics [PDF]
Briefings in Bioinformatics, 2016 In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields.
Seonwoo Min, Byunghan Lee, Sungroh Yoon
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Deep learning and geometric deep learning: An introduction for mathematicians and physicists
International Journal of Geometric Methods in Modern Physics, 2023 In this expository paper, we want to give a brief introduction, with few key references for further reading, to the inner functioning of the new and successful algorithms of Deep Learning and Geometric Deep Learning with a focus on Graph Neural Networks.
R. Fioresi, F. Zanchetta
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, 2023 Deep Learning (DL) has made a major impact on data science in the last decade. This chapter introduces the basic concepts of this field. It includes both the basic structures used to design deep neural networks and a brief survey of some of its popular use cases.
Lihi Shiloh-Perl, Raja Giryes
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Applied Sciences, 2021 To properly restore masonry cultural heritage sites, the materials used for retrofitting can have a critical effect, and this requires standards for traditional Korean brick and lime mortar to be examined.
Gayoon Lee +4 more
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Quantum Information and Computation, 2016 In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers.
Nathan Wiebe +2 more
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