Jiangchao Yao
Artificial Intelligence · 2018–2019
A researcher in the Science corpus: 3 papers; trusts 167 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 49 citations into it
- Computer Vision and Pattern Recognition — 48 citations into it
- Generative Models — 18 citations into it
- Optimization & Training — 13 citations into it
- Representation & Self-Supervised Learning — 13 citations into it
- Model Architectures — 8 citations into it
- Probabilistic & Bayesian Methods — 6 citations into it
Whom Jiangchao Yao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Kaiming He
- Alex Krizhevsky
- Karen Simonyan
- Max Welling
- Yann LeCun
- Radford M. Neal
- Ross B. Girshick
Who trusts Jiangchao Yao overall
Papers from Jiangchao Yao's perspective
Ordered by how strongly the researchers Jiangchao Yao 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
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Collaborative Learning for Weakly Supervised Object Detection (2018) — 50 citations
- How does Disagreement Help Generalization against Label Corruption? (2019) — 972 citations
- Degeneration in VAE: in the Light of Fisher Information Loss (2018) — 18 citations