Moontae Lee

Artificial Intelligence · 2015–2022

A researcher in the Science corpus: 4 papers; trusts 41 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 80 citations into it; #29,520 of 35,345 by trust here, #23,077 by citations
• Computer Vision and Pattern Recognition — 38 citations into it
• Natural Language Processing — 24 citations into it; #2,948 of 5,688 by trust here, #3,826 by citations
• Meta & Continual Learning — 18 citations into it
• Optimization & Training — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Model Architectures — 7 citations into it

Whom Moontae Lee trusts overall

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

1. Jason Aaron Edward Weston
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Paul Smolensky
5. Yoshua Bengio
6. Antoine Bordes
7. Sumit Chopra
8. Diederik P. Kingma
9. Ilya Sutskever
10. Max Welling

Who trusts Moontae Lee overall

1. Kezhen Chen
2. Paul Smolensky
3. Qiuyuan Huang
4. Divyansh Kaushik
5. Hamid Palangi
6. Bingning Wang
7. Jianfeng Gao
8. Rudolf Kadlec
9. Mehrad Moradshahi
10. Wojciech Kryscinski

Papers from Moontae Lee's perspective

Ordered by how strongly the researchers Moontae Lee 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Reasoning in Vector Space: An Exploratory Study of Question Answering (2015) — 39 citations
• Task-Balanced Batch Normalization for Exemplar-based Class-Incremental Learning (2022) — 3 citations
• Knowledge Unlearning for Mitigating Privacy Risks in Language Models (2022) — 490 citations
• Basic Reasoning with Tensor Product Representations (2016) — 32 citations