Rafael Rafailov

Artificial Intelligence · 2020–2024

A researcher in the Science corpus: 9 papers; trusts 264 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 320 citations into it; #25,726 of 35,345 by trust here, #10,065 by citations
• Reinforcement Learning — 182 citations into it
• Computer Vision and Pattern Recognition — 154 citations into it; #20,582 of 21,575 by trust here, #16,367 by citations
• Control and Systems Engineering — 75 citations into it
• Large Language Models — 49 citations into it
• Natural Language Processing — 44 citations into it
• Management Science and Operations Research — 40 citations into it
• Alignment & RLHF — 34 citations into it

Whom Rafael Rafailov trusts overall

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

1. Sergey Levine
2. Chelsea Finn
3. Diederik P. Kingma
4. Richard S. Sutton
5. Geoffrey E. Hinton
6. Ronald J. Williams
7. Yoshua Bengio
8. Pieter Abbeel
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Rafael Rafailov overall

1. Wei Xiong
2. Jiwoo Hong
3. Kevin Black
4. Bohan Wu
5. Hanze Dong
6. Anisha Gunjal
7. Haoxiang Wang
8. Tianhe Yu
9. Ruiqi Zhang
10. Ryan Park

Papers from Rafael Rafailov's perspective

Ordered by how strongly the researchers Rafael Rafailov 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

• COMBO: Conservative Offline Model-Based Policy Optimization (2021) — 535 citations
• Offline Reinforcement Learning from Images with Latent Space Models (2020) — 147 citations
• Diffusion Model Alignment Using Direct Preference Optimization (2023) — 819 citations
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 (2023) — 1,105 citations
• OpenVLA: An Open-Source Vision-Language-Action Model (2024) — 2,794 citations
• Offline Meta-Reinforcement Learning with Advantage Weighting (2020) — 125 citations
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model (2023) — 9,702 citations
• Disentangling Length from Quality in Direct Preference Optimization (2024) — 214 citations
• Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback (2023) — 810 citations