Guandao Yang
Computer Vision and Pattern Recognition · 2019–2020
A researcher in the Science corpus: 3 papers; trusts 129 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 89 citations into it
- Artificial Intelligence — 45 citations into it
- Generative Models — 41 citations into it; #1,246 of 1,956 by trust here, #668 by citations
- Computational Mechanics — 40 citations into it; #7,242 of 10,001 by trust here, #1,219 by citations
- Probabilistic & Bayesian Methods — 19 citations into it
- Optimization & Training — 15 citations into it
- Efficiency & Compression — 12 citations into it
- Model Architectures — 10 citations into it
Whom Guandao Yang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Yann LeCun
- Karen Simonyan
- Ilya Sutskever
- X. Zhang
- Max Welling
Who trusts Guandao Yang overall
- Julian Chibane
- Rundi Wu
- Linqi Zhou
- Shitong Luo
- Arash Vahdat
- Michael Niemeyer
- Tian Xie
- Weicheng Kuo
- Rameen Abdal
- Tong He
Papers from Guandao Yang's perspective
Ordered by how strongly the researchers Guandao Yang 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Learning Gradient Fields for Shape Generation (2020) — 341 citations
- PointFlow: 3D Point Cloud Generation With Continuous Normalizing Flows (2019) — 831 citations
- SWALP : Stochastic Weight Averaging in Low-Precision Training (2019) — 107 citations