Zekun Hao
Computer Vision and Pattern Recognition · 2017–2021
A researcher in the Science corpus: 4 papers; trusts 439 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 142 citations into it
- Generative Models — 62 citations into it; #1,247 of 1,956 by trust here, #668 by citations
- Computational Mechanics — 51 citations into it; #7,243 of 10,001 by trust here, #1,219 by citations
- Artificial Intelligence — 41 citations into it
- Computer Graphics and Computer-Aided Design — 38 citations into it; #1,100 of 1,584 by trust here, #234 by citations
- Probabilistic & Bayesian Methods — 19 citations into it
- Representation & Self-Supervised Learning — 11 citations into it
- Model Architectures — 8 citations into it
Whom Zekun Hao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Kaiming He
- Diederik P. Kingma
- Ross B. Girshick
- Alex Krizhevsky
- Christian Szegedy
- Yoshua Bengio
- Yann LeCun
- Sergey Ioffe
- Ilya Sutskever
Who trusts Zekun Hao overall
- Xiongwei Wu
- Zhong-Qiu Zhao
- William S. Peebles
- Yu Chen
- Konstantinos Rematas
- Julian Chibane
- Michael Niemeyer
- Xiaohui Zeng
- Jun Gao
- Arash Vahdat
Papers from Zekun Hao's perspective
Ordered by how strongly the researchers Zekun Hao 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)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds (2021) — 134 citations
- Scale-Aware Face Detection (2017) — 116 citations
- Learning Gradient Fields for Shape Generation (2020) — 341 citations
- PointFlow: 3D Point Cloud Generation With Continuous Normalizing Flows (2019) — 831 citations