Yuu Jinnai

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 4 papers; trusts 220 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 82 citations into it; #18,301 of 35,345 by trust here, #19,873 by citations
• Computer Vision and Pattern Recognition — 47 citations into it; #11,544 of 21,575 by trust here, #13,542 by citations
• Reinforcement Learning — 34 citations into it
• Model Architectures — 27 citations into it
• Neural Architecture Search & AutoML — 18 citations into it; #146 of 283 by trust here, #195 by citations
• Optimization & Training — 13 citations into it
• Management Science and Operations Research — 10 citations into it
• Computational Theory and Mathematics — 9 citations into it

Whom Yuu Jinnai trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Özgür Simsek
4. Richard M. Karp
5. Geoffrey E. Hinton
6. Marc G. Bellemare
7. Alex Krizhevsky
8. Doina Precup
9. Yoshua Bengio
10. Marlos C. Machado

Who trusts Yuu Jinnai overall

1. Martin Klissarov
2. Colin White
3. Marlos C. Machado
4. Doina Precup
5. Marc G. Bellemare
6. Yash Savani
7. Sam Nolen

Papers from Yuu Jinnai's perspective

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

Their papers

• AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search (2019) — 100 citations
• Learning to Prune Dominated Action Sequences in Online Black-Box Planning (2016) — 18 citations
• Discovering Options for Exploration by Minimizing Cover Time (2019) — 60 citations
• Neural Architecture Search Using Deep Neural Networks and Monte Carlo Tree Search (2018) — 56 citations