DeepLearning论文翻译（NatureDeepReview）DeepLearning论⽂翻译（NatureDeepReview）原论⽂出处：by Yann LeCun, Yoshua Bengio & Geoffrey Hinton Nature volume521, pages436–444 (28 May 2015）译者：零楚L（）这篇论⽂性质为深度学习的综述，原本只是想做做笔记，但找到的翻译都不怎么通顺。既然要啃原⽂...
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning.Nature521, 436–444 (2015). CASPubMedGoogle Scholar Nielsen, A. B. et al. Survival prediction in intensive-care units based on aggregation of long-term disease history and acute physiology: a retrospective study of the Danish National Pa...
Nature 521, 436–444 (2015). This article provides an overview of deep learning, highlighting its superior performance to traditional machine learning approaches. Article Google Scholar Bostrom, R., Sawyer, H. & Tolles, W. Instrumentation for automatically prescreening cytological smears. Proc. IRE...
[2]Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521(7553):436–444, 2015. [3]Maziar Raissi, Paris Perdikaris, and George E Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial diff...
521 Environmental Science & Technology Letters 522 Advances in Experimental Social Psychology 522 NEW ASTRONOMY REVIEWS 524 PERSONNEL PSYCHOLOGY 525 Brain Stimulation 526 CURRENT OPINION IN MICROBIOLOGY 527 LAB ON A CHIP 528 Epilepsy Currents 529 Nanophotonics 530 International Review of Sport and Exercise...
14. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015). 15. Raghu, M. & Schmidt, E. A survey of deep learning for scientific discovery. Preprint at https://arxiv.org/abs/2003.11755 (2020).
14. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015). 15. Raghu, M. & Schmidt, E. A survey of deep learning for scientific discovery. Preprint at https://arxiv.org/abs/2003.11755 (2020).
Nature volume 521, pages 436–444 (2015) https://www.nature.com/articles/nature14539 前半部分 Abstract 摘要 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 im...
Nature, 521(7553):436–444, 2015. [3]Maziar Raissi, Paris Perdikaris, and George E Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378:686–707, ...
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning.Nature521, 436–444 (2015). ADSCASPubMedGoogle Scholar Pichler, M., Boreux, V., Klein, A.-M., Schleuning, M. & Hartig, F. Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks....