Kimmo Kärkkäinen

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 763 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 104 citations into it; #10,169 of 21,575 by trust here, #12,172 by citations
• Artificial Intelligence — 34 citations into it
• Safety, Robustness & Interpretability — 31 citations into it
• Signal Processing — 26 citations into it; #3,156 of 7,572 by trust here, #3,099 by citations
• Evaluation & Benchmarks — 22 citations into it; #1,460 of 3,147 by trust here, #2,704 by citations
• Sociology and Political Science — 14 citations into it
• Generative Models — 13 citations into it
• Safety Research — 10 citations into it

Whom Kimmo Kärkkäinen trusts overall

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

1. Diederik P. Kingma
2. Kaiming He
3. Geoffrey E. Hinton
4. Zachary C. Steinert-Threlkeld
5. Alex Krizhevsky
6. Andrew Zisserman
7. Shaoqing Ren
8. Jimmy Ba
9. Yoshua Bengio
10. Yann LeCun

Who trusts Kimmo Kärkkäinen overall

1. Zaid Khan
2. Guha Balakrishnan
3. Weixin Liang
4. Dana Pessach
5. Abhishek Singh
6. Haoning Zhu
7. Yinglin Zheng
8. Yun Raymond Fu
9. Hugo Laurenccon
10. Alec Radford

Papers from Kimmo Kärkkäinen's perspective

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

Their papers

• FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation (2021) — 785 citations
• FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age (2019) — 206 citations