Kangwook Lee

Artificial Intelligence · 2013–2023

A researcher in the Science corpus: 10 papers; trusts 303 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 329 citations into it; #7,412 of 35,345 by trust here, #6,614 by citations
• Computer Vision and Pattern Recognition — 145 citations into it
• Natural Language Processing — 77 citations into it
• Large Language Models — 68 citations into it; #567 of 799 by trust here, #729 by citations
• Safety, Robustness & Interpretability — 63 citations into it; #1,532 of 2,251 by trust here, #1,329 by citations
• Optimization & Training — 62 citations into it; #2,231 of 2,601 by trust here, #2,519 by citations
• Computer Networks and Communications — 53 citations into it; #988 of 9,886 by trust here, #1,395 by citations
• Model Architectures — 36 citations into it

Whom Kangwook Lee trusts overall

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

1. L. Eon Bottou
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Kaiming He
6. Diederik P. Kingma
7. Yann LeCun
8. Ilya Sutskever
9. Ohad Shamir
10. Christian Szegedy

Who trusts Kangwook Lee overall

1. Zachary B. Charles
2. Deepesh Data
3. Amirhossein Reisizadeh
4. Sanghamitra Dutta
5. Can Bülent Karakus
6. Min Ye
7. Suhas N. Diggavi
8. Jungwoo Lee
9. Qian Yu
10. Rashish Tandon

Papers from Kangwook Lee's perspective

Ordered by how strongly the researchers Kangwook 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models (2023) — 460 citations
• Attack of the Tails: Yes, You Really Can Backdoor Federated Learning (2020) — 829 citations
• SLM: Learning a Discourse Language Representation with Sentence Unshuffling (2020) — 53 citations
• Looped Transformers as Programmable Computers (2023) — 222 citations
• LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks (2022) — 200 citations
• Debiasing Pre-Trained Language Models via Efficient Fine-Tuning (2022) — 49 citations
• Permutation-Based SGD: Is Random Optimal? (2021) — 16 citations
• FairBatch: Batch Selection for Model Fairness (2020) — 163 citations
• Speeding Up Distributed Machine Learning Using Codes (2015) — 924 citations
• When do redundant requests reduce latency ? (2013) — 191 citations