Zhenyi Wang

Artificial Intelligence · with Gao · 2020–2022

A researcher in the Science corpus: 8 papers; trusts 364 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 264 citations into it; #20,606 of 35,345 by trust here, #15,004 by citations
• Computer Vision and Pattern Recognition — 186 citations into it; #12,862 of 21,575 by trust here, #10,500 by citations
• Meta & Continual Learning — 89 citations into it
• Representation & Self-Supervised Learning — 76 citations into it
• Optimization & Training — 41 citations into it
• Probabilistic & Bayesian Methods — 32 citations into it
• Radiology, Nuclear Medicine and Imaging — 20 citations into it
• Natural Language Processing — 19 citations into it

Whom Zhenyi Wang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Chelsea Finn
6. Ilya Sutskever
7. Yann LeCun
8. Kaiming He
9. Max Welling
10. Oriol Vinyals

Who trusts Zhenyi Wang overall

1. Li Shen
2. Tianyu Liu
3. Le Fang
4. Liyuan Wang
5. Liangzu Peng
6. Tiehang Duan
7. Qiuling Suo
8. Rahul Aralikatte
9. Peng Wang
10. Chang Shu

Papers from Zhenyi Wang's perspective

Ordered by how strongly the researchers Zhenyi Wang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints (2020) — 90 citations
• Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness (2021) — 21 citations
• Learning to Learn and Remember Super Long Multi-Domain Task Sequence (2022) — 27 citations
• Improving Task-free Continual Learning by Distributionally Robust Memory Evolution (2022) — 54 citations
• Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions (2022) — 22 citations
• Meta-learning without data via Wasserstein distributionally-robust model fusion (2022) — 24 citations
• Meta-Learning with Neural Tangent Kernels (2021) — 21 citations
• Bayesian Meta Sampling for Fast Uncertainty Adaptation (2020) — 13 citations