Seungwook Kim

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 3 papers; trusts 297 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 53 citations into it; #16,948 of 35,345 by trust here, #24,417 by citations
• Computer Vision and Pattern Recognition — 45 citations into it; #10,741 of 21,575 by trust here, #16,367 by citations
• Representation & Self-Supervised Learning — 18 citations into it
• Optimization & Training — 17 citations into it
• Model Architectures — 12 citations into it
• Efficiency & Compression — 6 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom Seungwook Kim trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Kaiming He
5. Yoshua Bengio
6. Diederik P. Kingma
7. Ilya Sutskever
8. Ross B. Girshick
9. Sergey Ioffe
10. Christian Szegedy

Who trusts Seungwook Kim overall

1. Zheng Xu
2. Jianping Gou
3. Kibok Lee
4. Yen-Chang Hsu
5. Baosheng Yu
6. Tameem Adel
7. Jiawei Huang
8. Kimin Lee
9. Inyoung Paik
10. S. Maybank

Papers from Seungwook Kim's perspective

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

• Keep and Learn: Continual Learning by Constraining the Latent Space for Knowledge Preservation in Neural Networks (2018) — 31 citations
• DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning (2022) — 20 citations
• Transferring Knowledge to Smaller Network with Class-Distance Loss (2017) — 45 citations