Qizhe Xie
Artificial Intelligence · with Hovy · 2017–2020
A researcher in the Science corpus: 6 papers; trusts 726 researchers and is trusted by 1,041 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 273 citations into it; #911 of 35,345 by trust here, #1,221 by citations
- Computer Vision and Pattern Recognition — 216 citations into it; #1,456 of 21,575 by trust here, #1,584 by citations
- Representation & Self-Supervised Learning — 140 citations into it; #406 of 3,145 by trust here, #385 by citations
- Natural Language Processing — 80 citations into it; #884 of 5,688 by trust here, #509 by citations
- Optimization & Training — 54 citations into it
- Model Architectures — 44 citations into it
- Evaluation & Benchmarks — 29 citations into it; #719 of 3,147 by trust here, #540 by citations
- Generative Models — 13 citations into it
Whom Qizhe Xie trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Kaiming He
- Yann LeCun
- Ilya Sutskever
- Ruslan Salakhutdinov
- Christian Szegedy
- Jimmy Ba
Who trusts Qizhe Xie overall
- Quoc V. Le
- Ting-Li Chen
- Kihyuk Sohn
- Lihe Yang
- Golnaz Ghiasi
- Ishan Misra
- Timo Schick
- Mahmoud Assran
- Zhe Gan
- Mohit Bansal
Papers from Qizhe Xie's perspective
Ordered by how strongly the researchers Qizhe Xie 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
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Self-Training With Noisy Student Improves ImageNet Classification (2019) — 2,812 citations
- RACE: Large-scale ReAding Comprehension Dataset From Examinations (2017) — 1,655 citations
- Unsupervised Data Augmentation for Consistency Training (2019) — 2,706 citations
- Meta Pseudo Labels (2020) — 774 citations
- Controllable Invariance through Adversarial Feature Learning (2017) — 312 citations
- Unsupervised Data Augmentation (2019) — 219 citations