Jialin Mao

Publication Activity (10 Years)

Years Active: 2017-2023
Publications (10 Years): 17

Top Topics

Loss Minimization

Convolutional Neural Networks

Top Venues

Publications

Bingqing Sun, Jiaqiang Du, Fangfang Chong, Lijuan Li, Xiaoqian Zhu, Guangqing Zhai, Zebang Song, Jialin Mao
Spatio-Temporal Variation and Prediction of Carbon Storage in Terrestrial Ecosystems in the Yellow River Basin. Remote. Sens. 15 (15) (2023)
Jing Zhang, Jiaqiang Du, Shifeng Fang, Zhilu Sheng, Yangchengsi Zhang, Bingqing Sun, Jialin Mao, Lijuan Li
Dynamic Changes, Spatiotemporal Differences, and Ecological Effects of Impervious Surfaces in the Yellow River Basin, 1986-2020. Remote. Sens. 15 (1) (2023)
Rahul Ramesh, Jialin Mao, Itay Griniasty, Rubing Yang, Han Kheng Teoh, Mark K. Transtrum, James P. Sethna, Pratik Chaudhari
A Picture of the Space of Typical Learnable Tasks. ICML (2023)
Jialin Mao, Itay Griniasty, Han Kheng Teoh, Rahul Ramesh, Rubing Yang, Mark K. Transtrum, James P. Sethna, Pratik Chaudhari
The Training Process of Many Deep Networks Explores the Same Low-Dimensional Manifold. CoRR (2023)
Zhiqi Bu, Jialin Mao, Shiyun Xu
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy. NeurIPS (2022)
Jialin Mao, Michael Matheny, Philip Goodney, Art Sedrakyan
Using Linked Registry-Electronic Health Records to Examine Long-term Outcomes of Medical Devices. AMIA (2022)
Rubing Yang, Jialin Mao, Pratik Chaudhari
Does the Data Induce Capacity Control in Deep Learning? ICML (2022)
Sharon E. Davis, Henry Ssemaganda, Jejo Koola, Jialin Mao, Dax M. Westerman, Theodore Speroff, Usha Govindarajulu, Craig Ramsay, Lucila Ohno-Machado, Frederic S. Resnic, Michael E. Matheny
A Framework for Generating Synthetic Clinical Datasets with Learning Effects to Support Methods Development and Validation. AMIA (2022)
Jejo D. Koola, Jialin Mao, Sharon E. Davis, Henry Ssemaganda, Dax M. Westerman, Lucila Ohno-Machado, Frederic S. Resnic, Michael E. Matheny
A Framework for Detecting Medical Device Safety Signals Confounded by Learning Effects Using Machine Learning. AMIA (2022)
Henry Ssemaganda, Frederic S. Resnic, Sharon E. Davis, Usha Govindarajulu, Jejo Koola, Jialin Mao, Dax M. Westerman, Theodore Speroff, Craig Ramsay, Art Sedrakyan, Lucila Ohno-Machado, Michael E. Matheny
Disentangling and Characterizing Device Safety Signals and Learning Effects. AMIA (2022)
Rahul Ramesh, Jialin Mao, Itay Griniasty, Rubing Yang, Han Kheng Teoh, Mark K. Transtrum, James P. Sethna, Pratik Chaudhari
A picture of the space of typical learnable tasks. CoRR (2022)
Zhiqi Bu, Jialin Mao, Shiyun Xu
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy. CoRR (2022)
Rubing Yang, Jialin Mao, Pratik Chaudhari
Does the Data Induce Capacity Control in Deep Learning? CoRR (2021)
Sean Welleck, Zixin Yao, Yu Gai, Jialin Mao, Zheng Zhang, Kyunghyun Cho
Loss Functions for Multiset Prediction. NeurIPS (2018)
Sean Welleck, Jialin Mao, Kyunghyun Cho, Zheng Zhang
Saliency-based Sequential Image Attention with Multiset Prediction. NIPS (2017)
Sean Welleck, Zixin Yao, Yu Gai, Jialin Mao, Zheng Zhang, Kyunghyun Cho
Loss Functions for Multiset Prediction. CoRR (2017)
Sean Welleck, Jialin Mao, Kyunghyun Cho, Zheng Zhang
Saliency-based Sequential Image Attention with Multiset Prediction. CoRR (2017)