Hanul Shin
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 2 papers; trusts 53 researchers and is trusted by 434 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 25 citations into it; #1,509 of 35,345 by trust here, #1,566 by citations
- Computer Vision and Pattern Recognition — 21 citations into it; #2,027 of 21,575 by trust here, #1,174 by citations
- Representation & Self-Supervised Learning — 16 citations into it
- Meta & Continual Learning — 9 citations into it; #1,026 of 1,041 by trust here, #827 by citations
- Generative Models — 5 citations into it
Whom Hanul Shin trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Anthony V. Robins
- Yann LeCun
- Yoshua Bengio
- Radford M. Neal
- Diederik P. Kingma
- Individualized Treat
- Alex Krizhevsky
- Ruslan Salakhutdinov
- L. Eon Bottou
Who trusts Hanul Shin overall
- Zixuan Ke
- Mohammad Rostami
- Fei Ye
- Timothée Lesort
- Bing Liu
- Gido M. van de Ven
- Arthur Douillard
- Gyuhak Kim
- Jiangpeng He
- Umberto Michieli
Papers from Hanul Shin's perspective
Ordered by how strongly the researchers Hanul Shin trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
- GENERATIVE ADVERSARIAL NETS
Their papers
- Continual Learning with Deep Generative Replay (2017) — 2,523 citations
- Infinite Mixture Prototypes for Few-Shot Learning (2019) — 270 citations