Jung-Eun Shin

Molecular Biology · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 19 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 134 citations into it
• Computational Biology & Bioinformatics — 52 citations into it
• Radiology, Nuclear Medicine and Imaging — 51 citations into it
• Immunology — 37 citations into it
• Artificial Intelligence — 34 citations into it
• Natural Language Processing — 20 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Generative Models — 15 citations into it

Whom Jung-Eun Shin trusts overall

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

1. Geoffrey E. Hinton
2. Ilya Sutskever
3. Diederik P. Kingma
4. Yoshua Bengio
5. Jimmy Ba
6. Alex Graves
7. Sepp Hochreiter
8. Debora Marks
9. Yann LeCun
10. Tim Salimans

Who trusts Jung-Eun Shin overall

1. Ali Madani
2. Wengong Jin
3. Erik Nijkamp
4. Shitong Luo
5. Kevin E. Wu
6. Vladimir Gligorijevic
7. Kevin Kaichuang Yang
8. Christian Dallago
9. Jeffrey A. Ruffolo

Papers from Jung-Eun Shin's perspective

Ordered by how strongly the researchers Jung-Eun 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. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Protein design and variant prediction using autoregressive generative models (2021) — 363 citations
• Rapid generation of potent antibodies by autonomous hypermutation in yeast (2020) — 35 citations
• Accelerating Protein Design Using Autoregressive Generative Models (2019) — 45 citations