Hiroki Furuta

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 4 papers; trusts 108 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 216 citations into it
• Reinforcement Learning — 133 citations into it; #1,934 of 2,242 by trust here, #1,778 by citations
• Computer Vision and Pattern Recognition — 89 citations into it
• Control and Systems Engineering — 53 citations into it
• Computational Theory and Mathematics — 23 citations into it
• Electrical and Electronic Engineering — 19 citations into it
• Management Science and Operations Research — 19 citations into it
• Optimization & Training — 13 citations into it

Whom Hiroki Furuta 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. E. Todorov
6. Diederik P. Kingma
7. Pieter Abbeel
8. Yoshua Bengio
9. Alex Krizhevsky
10. Jan Peters

Who trusts Hiroki Furuta overall

1. Scott Fujimoto
2. Tomasz Korbak
3. Shixiang Shane Gu
4. Byung-Jun Lee
5. Boyu Gou
6. Tianhe Yu
7. Seyed Kamyar Seyed Ghasemipour
8. Boyuan Zheng
9. Hady ElSahar

Papers from Hiroki Furuta's perspective

Ordered by how strongly the researchers Hiroki Furuta 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

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
• A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis (2023) — 391 citations
• Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning (2021) — 14 citations