Jinsung Yoon

Artificial Intelligence · Neural Information Processing Systems · 2017–2021

A researcher in the Science corpus: 10 papers; trusts 426 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 126 citations into it; #10,628 of 35,345 by trust here, #13,353 by citations
• Computer Vision and Pattern Recognition — 86 citations into it; #9,101 of 21,575 by trust here, #10,905 by citations
• Representation & Self-Supervised Learning — 43 citations into it
• Safety, Robustness & Interpretability — 25 citations into it
• Optimization & Training — 24 citations into it
• Model Architectures — 21 citations into it
• Statistics and Probability — 19 citations into it; #8,026 of 12,932 by trust here, #7,914 by citations
• Generative Models — 18 citations into it; #864 of 1,956 by trust here, #1,502 by citations

Whom Jinsung Yoon trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Graves
4. Alex Krizhevsky
5. Ilya Sutskever
6. Kaiming He
7. Diederik P. Kingma
8. Yann LeCun
9. Ronald J. Williams
10. J. Urgen Schmidhuber

Who trusts Jinsung Yoon overall

1. Mihaela van der Schaar
2. Bryan Lim
3. Ioana Bica
4. Ahmed M. Alaa
5. James Jordon
6. Wenjie Du
7. Juan Miguel Lopez Alcaraz
8. Pierre-Alexandre Mattei
9. Y. Li
10. Oleg Ivanov

Papers from Jinsung Yoon's perspective

Ordered by how strongly the researchers Jinsung Yoon 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. 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. 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

• CutPaste: Self-Supervised Learning for Anomaly Detection and Localization (2021) — 1,213 citations
• GAIN: Missing Data Imputation using Generative Adversarial Nets (2018) — 1,410 citations
• Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks (2017) — 340 citations
• DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks (2018) — 819 citations
• Discovery and Clinical Decision Support for Personalized Healthcare (2017) — 79 citations
• VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain (2020) — 346 citations
• RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling (2019) — 11 citations
• Time-series Generative Adversarial Networks (2019) — 1,481 citations
• PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees (2018) — 850 citations
• GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets (2018) — 473 citations