Mingchen Gao

Artificial Intelligence · with Wang · 2021–2022

A researcher in the Science corpus: 5 papers; trusts 210 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 186 citations into it; #21,648 of 35,345 by trust here, #23,077 by citations
• Computer Vision and Pattern Recognition — 148 citations into it; #13,511 of 21,575 by trust here, #15,955 by citations
• Meta & Continual Learning — 80 citations into it
• Representation & Self-Supervised Learning — 68 citations into it
• Optimization & Training — 27 citations into it
• Reinforcement Learning — 14 citations into it
• Statistics and Probability — 13 citations into it

Whom Mingchen Gao trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Yoshua Bengio
5. Chelsea Finn
6. Yann LeCun
7. Kaiming He
8. Oriol Vinyals
9. Marc'Aurelio Ranzato
10. J. Urgen Schmidhuber

Who trusts Mingchen Gao overall

1. Zhenyi Wang

Papers from Mingchen Gao's perspective

Ordered by how strongly the researchers Mingchen Gao 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• 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