Ryan Spring 个人学术档案 - 学术资源搜索

2016201720182019202020212022202320244 10 33 41 67 86 89 71 40

开放获取的出版物数量

5 篇文章

0 篇文章

可查看的文章

无法查看的文章

根据资助方的强制性开放获取政策

Anshumali ShrivastavaRice University, ThirdAI Corp.在 rice.edu 的电子邮件经过验证
Bryan CatanzaroNVIDIA在 acm.org 的电子邮件经过验证
Raul PuriResearch Scientist, OpenAI在 openai.com 的电子邮件经过验证
Mostofa PatwaryApplied Deep Learning Research, NVIDIA在 nvidia.com 的电子邮件经过验证
Mohammad ShoeybiDirector of Applied Research at NVIDIA在 nvidia.com 的电子邮件经过验证
Gautam DasarathyAssistant Professor, Electrical Engineering, Arizona State University在 asu.edu 的电子邮件经过验证
Amirali AghazadehECE, Georgia Tech在 gatech.edu 的电子邮件经过验证
Anastasios KyrillidisAssociate Professor, Computer Science Department, Ken Kennedy Institute Fellow, Rice University在 rice.edu 的电子邮件经过验证
Richard BaraniukC. Sidney Burrus Professor, Electrical & Computer Engineering, Rice University在 rice.edu 的电子邮件经过验证

Ryan Spring

在 rice.edu 的电子邮件经过验证 - 首页


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Scalable and sustainable deep learning via randomized hashing R Spring, A Shrivastava Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017	150	2017
Training Question Answering Models From Synthetic Data R Puri, R Spring, M Patwary, M Shoeybi, B Catanzaro arXiv preprint arXiv: 2002.09599, 2020	145	2020
A new unbiased and efficient class of lsh-based samplers and estimators for partition function computation in log-linear models R Spring, A Shrivastava arXiv preprint arXiv:1703.05160, 2017	51	2017
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches A Aghazadeh, R Spring, D LeJeune, G Dasarathy, A Shrivastava, ... Proceedings of the 35th International Conference on Machine Learning 80, 80-88, 2018	41	2018
Compressing Gradient Optimizers via Count-Sketches R Spring, A Kyrillidis, V Mohan, A Shrivastava Proceedings of the 36th International Conference on Machine Learning 97 …, 2019	34	2019
Mutual Information Estimation using LSH Sampling. R Spring, A Shrivastava IJCAI, 2807-2815, 2020	14	2020
Training question answering models from synthetic data. arXiv 2020 R Puri, R Spring, M Patwary, M Shoeybi, B Catanzaro arXiv preprint arXiv:2002.09599, 0	5
Resource-Efficient Machine Learning via Count-Sketches and Locality-Sensitive Hashing (LSH) RD Spring Rice University, 2020	1	2020

系统目前无法执行此操作，请稍后再试。

文章 1–8

每年引用数