Jiaming Xu

Artificial Intelligence · with Su · 2017–2018

A researcher in the Science corpus: 4 papers; trusts 42 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 58 citations into it; #4,294 of 35,345 by trust here, #7,606 by citations
• Statistics and Probability — 46 citations into it
• Optimization & Training — 27 citations into it; #1,066 of 2,601 by trust here, #1,248 by citations
• Computational Mechanics — 21 citations into it
• Statistical Learning Theory — 20 citations into it
• Safety, Robustness & Interpretability — 14 citations into it
• Computer Networks and Communications — 13 citations into it; #5,954 of 9,886 by trust here, #5,961 by citations
• ML Systems & Hardware — 10 citations into it; #138 of 575 by trust here, #368 by citations

Whom Jiaming Xu trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yudong Chen
2. Jacob Steinhardt
3. Terence Tao
4. Jakub Konecný
5. Lili Su
6. Roman Vershynin
7. Jiashi Feng
8. John C. Duchi
9. Martin J. Wainwright
10. Moses Charikar

Who trusts Jiaming Xu overall

1. Cong Xie
2. Deepesh Data
3. Lingjuan Lyu
4. Dong Yin
5. Lili Su
6. Oluwasanmi Koyejo
7. Zhaoxian Wu
8. Xinyang Cao
9. Lie He
10. Sai Praneeth Karimireddy

Papers from Jiaming Xu's perspective

Ordered by how strongly the researchers Jiaming Xu trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Distributed Statistical Machine Learning in Adversarial Settings (2017) — 293 citations
• Securing Distributed Machine Learning in High Dimensions (2018) — 37 citations
• Securing Distributed Gradient Descent in High Dimensional Statistical Learning (2018) — 65 citations
• Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent (2017) — 599 citations