Exascale Deep Learning for Climate Analytics
暂无分享,去创建一个
Prabhat | Massimiliano Fatica | Thorsten Kurth | Joshua Romero | Jack Deslippe | Everett H. Phillips | Sean Treichler | Michael Houston | Michael Matheson | Nathan Luehr | Mayur Mudigonda | Michael A. Matheson | Ankur Mahesh | Sean Treichler | M. Fatica | J. Deslippe | M. Mudigonda | T. Kurth | A. Mahesh | Michael Houston | Nathan Luehr | E. Phillips | J. Romero | Prabhat Prabhat | P. Prabhat
[1] Vipin Kumar,et al. Big Data in Climate: Opportunities and Challenges for Machine Learning , 2016, SIGIR.
[2] Daniel Walton,et al. Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design , 2018, Geoscientific Model Development.
[3] Takuya Akiba,et al. Extremely Large Minibatch SGD: Training ResNet-50 on ImageNet in 15 Minutes , 2017, ArXiv.
[4] Prabhat,et al. A deep generative model for astronomical images of galaxies , 2015 .
[5] Yuanzhou Yang,et al. Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes , 2018, ArXiv.
[6] Yann LeCun,et al. Deep learning with Elastic Averaging SGD , 2014, NIPS.
[7] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[8] Kazuhiro Terao,et al. Machine learning at the energy and intensity frontiers of particle physics , 2018, Nature.
[9] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Prabhat,et al. Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets , 2016, ArXiv.
[11] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[12] Sa-Kwang Song,et al. GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from Remote Sensing Imagery , 2017, ArXiv.
[13] Surendra Byna,et al. TECA: Petascale Pattern Recognition for Climate Science , 2015, CAIP.
[14] Oliver Hennigh,et al. Lat-Net: Compressing Lattice Boltzmann Flow Simulations using Deep Neural Networks , 2017, 1705.09036.
[15] Yang You,et al. Large Batch Training of Convolutional Networks , 2017, 1708.03888.
[16] Prabhat,et al. CosmoFlow: Using Deep Learning to Learn the Universe at Scale , 2018, SC18: International Conference for High Performance Computing, Networking, Storage and Analysis.
[17] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[18] George Papandreou,et al. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.
[19] W S McCulloch,et al. A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.
[20] Yoshua Bengio,et al. The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[21] Alán Aspuru-Guzik,et al. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.
[22] Alexander Sergeev,et al. Horovod: fast and easy distributed deep learning in TensorFlow , 2018, ArXiv.
[23] Moritz Müller,et al. Deep Learning in Science , 2020, ArXiv.
[24] Takuya Akiba,et al. ChainerMN: Scalable Distributed Deep Learning Framework , 2017, ArXiv.
[25] Julia Ling,et al. Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework , 2017, Physical Review Fluids.
[26] Surendra Byna,et al. TECA: A Parallel Toolkit for Extreme Climate Analysis , 2012, ICCS.
[27] Daniel George,et al. Deep Neural Networks to Enable Real-time Multimessenger Astrophysics , 2016, ArXiv.