A Geometric Understanding of Deep Learning
Na Lei,Dongsheng An,Yang Guo,Kehua Su,Shixia Liu,Zhongxuan Luo,Shing-Tung Yau,Xianfeng Gu,Xianfeng Gu +8 more
TLDR
In this article, an optimal transportation (OT) view of GANs is introduced, where the generator computes the OT map and the discriminator computes Wasserstein distance between the generated data distribution and the real data distribution.About:
This article is published in Engineering.The article was published on 2020-03-01 and is currently open access. It has received 58 citations till now. The article focuses on the topics: Generative model & Probability distribution.read more
Citations
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Deep learning for geophysics: Current and future trends
Siwei Yu,Jianwei Ma +1 more
TL;DR: A new data-driven technique, i.e., deep learning (DL), has attracted significantly increasing attention in the geophysical community and the collision of DL and traditional methods has had an impact on traditional methods.
Journal ArticleDOI
A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis.
Lian Zou,Shaode Yu,Shaode Yu,Tiebao Meng,Zhicheng Zhang,Xiaokun Liang,Xiaokun Liang,Yaoqin Xie +7 more
TL;DR: Conclusively, CNN-based MBCD is at its early stage, and there is still a long way ahead in achieving the ultimate goal of using deep learning tools to facilitate clinical practice.
Posted Content
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System
TL;DR: A deep learning model is proposed to construct new balanced representations of the imbalanced datasets of the ICS datasets and leverages Deep Neural Network (DNN) and Decision Tree (DT) classifiers to detect cyber-attacks from the new representations.
Journal ArticleDOI
FocusNetv2: Imbalanced large and small organ segmentation with adversarial shape constraint for head and neck CT images.
Yunhe Gao,Rui Huang,Yiwei Yang,Jie Zhang,Kainan Shao,Chang-Juan Tao,Yuanyuan Chen,Dimitris N. Metaxas,Hongsheng Li,Ming Chen +9 more
TL;DR: A novel two-stage deep neural network, FocusNetv2, is proposed to solve this challenging problem by automatically locating, ROI-pooling, and segmenting small organs with specifically designed small-organ localization and segmentation sub-networks while maintaining the accuracy of large organ segmentation.
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Reparameterized full-waveform inversion using deep neural networks
Qinglong He,Yanfei Wang +1 more
TL;DR: In this paper, the misfit function of the conventional FWI method (metric l2-norm) was analyzed and it was shown that it is a powerful method for providing a high-resolution description of the subsurface.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Journal Article
Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
Posted Content
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
Proceedings Article
Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.