Tatsuya Matsushima

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 91 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 152 citations into it
• Reinforcement Learning — 123 citations into it; #1,933 of 2,242 by trust here, #1,778 by citations
• Computer Vision and Pattern Recognition — 64 citations into it
• Control and Systems Engineering — 50 citations into it
• Computational Theory and Mathematics — 23 citations into it
• Management Science and Operations Research — 19 citations into it
• Electrical and Electronic Engineering — 18 citations into it
• Optimization & Training — 12 citations into it

Whom Tatsuya Matsushima trusts overall

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

1. Sergey Levine
2. Richard S. Sutton
3. John Schulman
4. Geoffrey E. Hinton
5. Aviral Kumar
6. Diederik P. Kingma
7. Pieter Abbeel
8. E. Todorov
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts Tatsuya Matsushima overall

1. Byung-Jun Lee
2. Tianhe Yu
3. Seyed Kamyar Seyed Ghasemipour
4. Scott Fujimoto
5. Shixiang Shane Gu
6. Rafael Rafailov
7. Aviral Kumar
8. Jongmin Lee
9. Dale Schuurmans

Papers from Tatsuya Matsushima's perspective

Ordered by how strongly the researchers Tatsuya Matsushima 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization (2020) — 166 citations
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 (2023) — 1,105 citations
• Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning (2021) — 14 citations