Zijun Huang
Artificial Intelligence · 2018–2019
A researcher in the Science corpus: 3 papers; trusts 491 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 98 citations into it; #8,863 of 35,345 by trust here, #5,970 by citations
- Computer Vision and Pattern Recognition — 97 citations into it; #8,791 of 21,575 by trust here, #5,507 by citations
- Representation & Self-Supervised Learning — 68 citations into it; #1,587 of 3,145 by trust here, #1,021 by citations
- Generative Models — 31 citations into it
- Signal Processing — 11 citations into it
- Cancer Research — 10 citations into it; #352 of 1,780 by trust here, #621 by citations
- Optimization & Training — 10 citations into it
- Evaluation & Benchmarks — 9 citations into it
Whom Zijun Huang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yann LeCun
- Yoshua Bengio
- Ilya Sutskever
- Ross B. Girshick
- Karen Simonyan
- Christian Szegedy
Who trusts Zijun Huang overall
- Jian Liang
- Xingchao Peng
- Dapeng Hu
- Peng Su
- Ying Jin
- Ananya Kumar
- Ziwei Liu
- Junyuan Hong
- Detai Xin
- Hangyu Zhu
Papers from Zijun Huang's perspective
Ordered by how strongly the researchers Zijun Huang trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Moment Matching for Multi-Source Domain Adaptation (2018) — 2,338 citations
- Domain Agnostic Learning with Disentangled Representations (2019) — 304 citations
- Federated Adversarial Domain Adaptation (2019) — 319 citations