Zhizhong Li
Artificial Intelligence · with Hoiem · 2016–2022
A researcher in the Science corpus: 6 papers; trusts 892 researchers and is trusted by 3,936 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 165 citations into it; #359 of 35,345 by trust here, #880 by citations
- Computer Vision and Pattern Recognition — 159 citations into it; #534 of 21,575 by trust here, #710 by citations
- Model Architectures — 47 citations into it
- Efficiency & Compression — 41 citations into it
- Representation & Self-Supervised Learning — 36 citations into it; #29 of 3,145 by trust here, #120 by citations
- Evaluation & Benchmarks — 27 citations into it
- Optimization & Training — 25 citations into it
- Safety, Robustness & Interpretability — 21 citations into it
Whom Zhizhong Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Kaiming He
- Ross B. Girshick
- Christian Szegedy
- Ilya Sutskever
- Diederik P. Kingma
- Yann LeCun
- Karen Simonyan
Who trusts Zhizhong Li overall
- Rahaf Aljundi
- Da-Wei Zhou
- Vincenzo Lomonaco
- Fei Ye
- Marc Masana
- Yaoyao Liu
- Tinne Tuytelaars
- Han-Jia Ye
- Xialei Liu
- Gyuhak Kim
Papers from Zhizhong Li's perspective
Ordered by how strongly the researchers Zhizhong Li 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)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Learning without Forgetting (2016) — 5,765 citations
- Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion (2019) — 706 citations
- PolyNet: A Pursuit of Structural Diversity in Very Deep Networks (2016) — 279 citations
- ViM: Out-Of-Distribution with Virtual-logit Matching (2022) — 549 citations
- Class-Incremental Learning with Strong Pre-trained Models (2022) — 94 citations
- Improving Confidence Estimates for Unfamiliar Examples (2018) — 43 citations