Seonwoo Min

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 240 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it
• Computer Vision and Pattern Recognition — 49 citations into it
• Molecular Biology — 43 citations into it; #4,757 of 17,773 by trust here, #6,283 by citations
• Optimization & Training — 29 citations into it
• Model Architectures — 26 citations into it
• Natural Language Processing — 16 citations into it
• Computational Biology & Bioinformatics — 12 citations into it
• Representation & Self-Supervised Learning — 12 citations into it

Whom Seonwoo Min trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Graves
4. Diederik P. Kingma
5. Yann LeCun
6. J. Urgen Schmidhuber
7. Ilya Sutskever
8. Alex Krizhevsky
9. L. Eon Bottou
10. Sepp Hochreiter

Who trusts Seonwoo Min overall

1. A. Navon
2. Byunghan Lee
3. Mufti Mahmud
4. Ehsaneddin Asgari
5. Alexandru Korotcov
6. Bilal Mirza
7. Xiao Wang
8. Yosi Keller
9. Andrés Camero
10. Sameer Quazi

Papers from Seonwoo Min's perspective

Ordered by how strongly the researchers Seonwoo Min 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deep learning in bioinformatics (2016) — 1,486 citations
• deepMiRGene: Deep Neural Network based Precursor microRNA Prediction (2016) — 50 citations
• Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity (2018) — 347 citations