Saehoon Kim

Artificial Intelligence · with Yang · 2018–2022

A researcher in the Science corpus: 7 papers; trusts 378 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it; #10,546 of 35,345 by trust here, #8,210 by citations
• Computer Vision and Pattern Recognition — 113 citations into it
• Representation & Self-Supervised Learning — 69 citations into it
• Optimization & Training — 27 citations into it
• Generative Models — 21 citations into it
• Meta & Continual Learning — 21 citations into it; #329 of 1,041 by trust here, #369 by citations
• Model Architectures — 18 citations into it
• Reinforcement Learning — 16 citations into it

Whom Saehoon Kim 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. Kaiming He
7. Ilya Sutskever
8. Karen Simonyan
9. Oriol Vinyals
10. Ruslan Salakhutdinov

Who trusts Saehoon Kim overall

1. Jaehong Yoon
2. Sen Lin
3. Gobinda Saha
4. Li Yang
5. Jorge Armando Mendez Mendez
6. Divyam Madaan
7. Hao Liu
8. Gyuhak Kim
9. Xikun Zhang
10. Hyun-woo Koh

Papers from Saehoon Kim's perspective

Ordered by how strongly the researchers Saehoon Kim 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. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning to Propagate Labels: Transductive Propagation Network for Few-Shot Learning (2018) — 758 citations
• Autoregressive Image Generation using Residual Quantization (2022) — 851 citations
• Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks (2019) — 116 citations
• ORACLE: Order Robust Adaptive Continual LEarning (2019) — 9 citations
• Bayesian Optimization over Sets (2019) — 9 citations
• Scalable and Order-robust Continual Learning with Additive Parameter Decomposition (2019) — 211 citations
• Transductive Propagation Network for Few-shot Learning (2018) — 81 citations