Jiaxiang Wu
Artificial Intelligence · with Huang · 2018–2021
A researcher in the Science corpus: 2 papers; trusts 32 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 21 citations into it; #18,916 of 35,345 by trust here, #19,439 by citations
- Molecular Biology — 21 citations into it
- Computer Vision and Pattern Recognition — 20 citations into it
- Materials Chemistry — 20 citations into it
- Optimization & Training — 13 citations into it; #1,625 of 2,601 by trust here, #1,715 by citations
- Computational Biology & Bioinformatics — 9 citations into it
- Model Architectures — 9 citations into it
- Computational Theory and Mathematics — 6 citations into it
Whom Jiaxiang Wu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yang Song
- Kaiming He
- Jimmy Ba
- Max Welling
- Yoshua Bengio
- Chih-Jen Lin
- Yann LeCun
Who trusts Jiaxiang Wu overall
- Yuqing Du
- Sebastian U. Stich
- Hanlin Tang
- Constantin Philippenko
- Shuai Zheng
- Haftay Gebreslasie Abreha
- Hyeontaek Lim
- Farzin Haddadpour
- Sai Praneeth Karimireddy
- Debraj Basu
Papers from Jiaxiang Wu's perspective
Ordered by how strongly the researchers Jiaxiang Wu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization (2018) — 255 citations
- SE(3)-Equivariant Energy-based Models for End-to-End Protein Folding (2021) — 11 citations