Kevin J Liang

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 373 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 114 citations into it; #25,950 of 35,345 by trust here, #18,586 by citations
• Computer Vision and Pattern Recognition — 101 citations into it; #15,930 of 21,575 by trust here, #12,419 by citations
• Generative Models — 42 citations into it; #1,634 of 1,956 by trust here, #1,715 by citations
• Meta & Continual Learning — 35 citations into it
• Representation & Self-Supervised Learning — 35 citations into it
• Optimization & Training — 22 citations into it
• Model Architectures — 15 citations into it
• Reinforcement Learning — 15 citations into it

Whom Kevin J Liang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Karen Simonyan
9. Radford M. Neal
10. Jimmy Ba

Who trusts Kevin J Liang overall

1. Nikhil Mehta
2. Vinay Kumar Verma
3. Ting-Li Chen
4. Xu He
5. Lawrence Carin
6. Hoang Thanh-Tung
7. Liyuan Wang
8. Xiaohua Zhai

Papers from Kevin J Liang's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
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

• Generative Adversarial Network Training is a Continual Learning Problem (2018) — 53 citations
• Efficient Feature Transformations for Discriminative and Generative Continual Learning (2021) — 93 citations
• Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors (2021)
• Bayesian Nonparametric Weight Factorization for Continual Learning (2020) — 5 citations