Frederick Liu

Artificial Intelligence · 2016–2023

A researcher in the Science corpus: 8 papers; trusts 183 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 230 citations into it; #16,697 of 35,345 by trust here, #5,252 by citations
• Natural Language Processing — 95 citations into it; #2,952 of 5,688 by trust here, #3,154 by citations
• Safety, Robustness & Interpretability — 77 citations into it
• Computer Vision and Pattern Recognition — 63 citations into it; #14,386 of 21,575 by trust here, #13,269 by citations
• Safety Research — 22 citations into it
• Privacy & Security — 21 citations into it
• Sociology and Political Science — 20 citations into it
• Information Systems — 15 citations into it

Whom Frederick Liu trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Yann LeCun
6. Kaiming He
7. Scott M. Lundberg
8. Ilya Sutskever
9. Been Kim
10. Jimmy Ba

Who trusts Frederick Liu overall

1. Gabriel Erion-Barner
2. Aida Mostafazadeh Davani
3. Xi Ye
4. Felix Stahlberg
5. Jean-Benoit Delbrouck
6. Iacer Calixto
7. Xiaochuang Han
8. Karthikeyan K
9. Joseph D. Janizek
10. Lucia Specia

Papers from Frederick Liu's perspective

Ordered by how strongly the researchers Frederick Liu 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• PaLM 2 Technical Report (2023) — 1,524 citations
• Attention-based Multimodal Neural Machine Translation (2016) — 183 citations
• Tracing Knowledge in Language Models Back to the Training Data (2022) — 29 citations
• Incorporating Priors with Feature Attribution on Text Classification (2019) — 133 citations
• Estimating Training Data Influence by Tracking Gradient Descent (2020) — 704 citations
• The Creation and Analysis of a Website Privacy Policy Corpus (2016) — 346 citations
• Towards Tracing Knowledge in Language Models Back to the Training Data (2022) — 71 citations
• Differentially-Private "Draw and Discard" Machine Learning (2018) — 39 citations