Seung-Jae Shin

Computer Vision and Pattern Recognition · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 67 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 33 citations into it
• Artificial Intelligence — 30 citations into it
• Generative Models — 23 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Statistical and Nonlinear Physics — 9 citations into it

Whom Seung-Jae Shin trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Yoshua Bengio
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. Max Welling
9. Tomas Mikolov
10. Gregory R. Koch

Papers from Seung-Jae Shin's perspective

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

Their papers

• Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation (2021) — 118 citations
• Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation (2020) — 23 citations