Cong Xu

Artificial Intelligence · with Chen · 2017–2018

A researcher in the Science corpus: 4 papers; trusts 204 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 74 citations into it; #15,834 of 35,345 by trust here, #6,014 by citations
• Computer Vision and Pattern Recognition — 72 citations into it
• Optimization & Training — 60 citations into it; #1,461 of 2,601 by trust here, #654 by citations
• Efficiency & Compression — 32 citations into it
• Model Architectures — 19 citations into it
• Scaling & Learning Theory — 17 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom Cong Xu trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yann LeCun
5. Diederik P. Kingma
6. Christian Szegedy
7. Karen Simonyan
8. Ilya Sutskever
9. Yoshua Bengio
10. L. Eon Bottou

Who trusts Cong Xu overall

1. Konstantin Mishchenko
2. Peter Richtárik
3. Sebastian U. Stich
4. Hanlin Tang
5. Ahmed Khaled
6. Yunhui Guo
7. By Lei Deng
8. Sai Praneeth Karimireddy
9. Hao Yu
10. Jakub Konecný

Papers from Cong Xu's perspective

Ordered by how strongly the researchers Cong 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Coordinating Filters for Faster Deep Neural Networks (2017) — 143 citations
• SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning (2018) — 55 citations
• TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning (2017) — 1,084 citations
• SmoothOut: Smoothing Out Sharp Minima for Generalization in Large-Batch Deep Learning (2018) — 8 citations