Huiyi Hu
Artificial Intelligence · 2019–2022
A researcher in the Science corpus: 6 papers; trusts 341 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 197 citations into it; #11,734 of 35,345 by trust here, #11,750 by citations
- Computer Vision and Pattern Recognition — 145 citations into it; #8,024 of 21,575 by trust here, #9,187 by citations
- Meta & Continual Learning — 82 citations into it
- Optimization & Training — 50 citations into it; #1,394 of 2,601 by trust here, #1,488 by citations
- Representation & Self-Supervised Learning — 36 citations into it
- Reinforcement Learning — 34 citations into it
- Model Architectures — 28 citations into it
- Probabilistic & Bayesian Methods — 24 citations into it
Whom Huiyi Hu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Kaiming He
- Diederik P. Kingma
- Yoshua Bengio
- Yann LeCun
- Alex Graves
- Ilya Sutskever
- Ronald J. Williams
- Radford M. Neal
Who trusts Huiyi Hu overall
- Youngjae Min
- Thang Van Doan
- Mehrdad Farajtabar
- Yeming Wen
- Seyed Iman Mirzadeh
- Justin N. M. Pinkney
- Saurabh Garg
- Pavel Izmailov
- Dustin Tran
- Florian Wenzel
Papers from Huiyi Hu's perspective
Ordered by how strongly the researchers Huiyi Hu 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
- Deep Ensembles: A Loss Landscape Perspective (2019) — 770 citations
- Wide Neural Networks Forget Less Catastrophically (2021) — 88 citations
- Task-agnostic Continual Learning with Hybrid Probabilistic Models (2021) — 24 citations
- Information-theoretic Online Memory Selection for Continual Learning (2022) — 64 citations
- One Pass ImageNet (2021) — 21 citations
- Cross-View Policy Learning for Street Navigation (2019) — 37 citations