Robert Dadashi

Artificial Intelligence · 2019–2025

A researcher in the Science corpus: 12 papers; trusts 156 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 324 citations into it
• Reinforcement Learning — 257 citations into it
• Computer Vision and Pattern Recognition — 81 citations into it
• Control and Systems Engineering — 44 citations into it
• Optimization & Training — 43 citations into it
• Model Architectures — 31 citations into it
• Management Science and Operations Research — 27 citations into it
• Computational Theory and Mathematics — 23 citations into it

Whom Robert Dadashi trusts overall

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

1. Richard S. Sutton
2. Sergey Levine
3. Diederik P. Kingma
4. Dimitri P. Bertsekas
5. Ronald J. Williams
6. Geoffrey E. Hinton
7. Martin L. Puterman
8. Andrew Y. Ng
9. Dean A. Pomerleau
10. Pieter Abbeel

Who trusts Robert Dadashi overall

1. L'eonard Hussenot
2. Tim Pearce
3. Michael Janner

Papers from Robert Dadashi's perspective

Ordered by how strongly the researchers Robert Dadashi 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. GENERATIVE ADVERSARIAL NETS
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

• The Value Function Polytope in Reinforcement Learning (2019) — 57 citations
• A Geometric Perspective on Optimal Representations for Reinforcement Learning (2019) — 99 citations
• Offline Reinforcement Learning with Pseudometric Learning (2021) — 44 citations
• Gemma 2: Improving Open Language Models at a Practical Size (2024) — 2,206 citations
• Primal Wasserstein Imitation Learning (2020) — 157 citations
• Gemma: Open Models Based on Gemini Research and Technology (2024) — 1,150 citations
• What Matters for Adversarial Imitation Learning? (2021) — 102 citations
• Hyperparameter Selection for Imitation Learning (2021) — 21 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Gemma 3 Technical Report (2025) — 1,621 citations
• MedGemma Technical Report (2025) — 365 citations
• Continuous Control with Action Quantization from Demonstrations (2021) — 33 citations