Stephen Seung-Yeob Baek

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 2 papers; trusts 19 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 48 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it
• Safety, Robustness & Interpretability — 13 citations into it
• Natural Language Processing — 9 citations into it
• Health Informatics — 8 citations into it
• Model Architectures — 7 citations into it

Whom Stephen Seung-Yeob Baek trusts overall

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

1. Diederik P. Kingma
2. Cynthia Rudin
3. Dzmitry Bahdanau
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Jimmy Ba
7. Andrej Karpathy
8. Nils Reimers
9. Alex Krizhevsky
10. Marco Tulio Ribeiro

Papers from Stephen Seung-Yeob Baek's perspective

Ordered by how strongly the researchers Stephen Seung-Yeob Baek trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Going deeper with convolutions

Their papers

• Federated learning enables big data for rare cancer boundary detection (2022) — 346 citations
• Interpretable Sequence Classification Via Prototype Trajectory (2020) — 10 citations