Chankyu Lee

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 2 papers; trusts 12 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 58 citations into it; #32,150 of 35,345 by trust here, #12,761 by citations
• Electrical and Electronic Engineering — 24 citations into it; #13,829 of 16,539 by trust here, #12,727 by citations
• Computer Vision and Pattern Recognition — 22 citations into it
• Cognitive Neuroscience — 19 citations into it; #9,105 of 10,852 by trust here, #8,363 by citations
• Model Architectures — 10 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Optimization & Training — 5 citations into it

Whom Chankyu Lee trusts overall

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

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

Who trusts Chankyu Lee overall

1. Wei Fang
2. Nitin Rathi
3. Hayato Tsukagoshi
4. Hanle Zheng
5. Zhaofei Yu
6. Kaushik Roy
7. Ryohei Sasano
8. Yanqing Chen

Papers from Chankyu Lee's perspective

Ordered by how strongly the researchers Chankyu Lee 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures (2019) — 452 citations
• NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models (2024) — 542 citations