Yutaro Yamada

Artificial Intelligence · 2015–2025

A researcher in the Science corpus: 4 papers; trusts 114 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it
• Reinforcement Learning — 37 citations into it
• Computer Vision and Pattern Recognition — 32 citations into it
• Safety, Robustness & Interpretability — 20 citations into it; #575 of 2,251 by trust here, #930 by citations
• Optimization & Training — 13 citations into it; #896 of 2,601 by trust here, #988 by citations
• Control and Systems Engineering — 11 citations into it
• Natural Language Processing — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it

Whom Yutaro Yamada trusts overall

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

1. Yoshua Bengio
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yann LeCun
5. Diederik P. Kingma
6. Ilya Sutskever
7. Sergey Levine
8. Kaiming He
9. Richard S. Sutton
10. Yurii E. Nesterov

Who trusts Yutaro Yamada overall

1. Moustapha Cissé
2. Pierre Stock
3. Nicholas Carlini
4. Jason Jo
5. Sungyeob Han
6. Xin Li
7. A. Serban
8. Dong Yin
9. Michihiro Yasunaga
10. Yifei Min

Papers from Yutaro Yamada's perspective

Ordered by how strongly the researchers Yutaro Yamada 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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

• Can Wikipedia Help Offline Reinforcement Learning? (2022) — 104 citations
• The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search (2025) — 314 citations
• Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization (2015) — 378 citations
• Understanding adversarial training: Increasing local stability of supervised models through robust optimization (2015) — 104 citations