Do-Yeon Kim

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 2 papers; trusts 85 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 46 citations into it
• Computer Vision and Pattern Recognition — 36 citations into it
• Meta & Continual Learning — 29 citations into it
• Representation & Self-Supervised Learning — 13 citations into it

Whom Do-Yeon Kim trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Oriol Vinyals
5. Spyros Gidaris
6. Diederik P. Kingma
7. Yoshua Bengio
8. Yann LeCun
9. Ross B. Girshick
10. Da-Wei Zhou

Who trusts Do-Yeon Kim overall

1. Song Tian
2. Keon-Hee Park
3. Lusi Li
4. Haoxuan Qu
5. Kyungwoo Song
6. S. Divakar Bhat
7. Afra Feyza Akyürek
8. Zhen Tan

Papers from Do-Yeon Kim's perspective

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

Their papers

• XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning (2020) — 49 citations
• Warping the Space: Weight Space Rotation for Class-Incremental Few-Shot Learning (2023) — 75 citations