Dorsa Sadigh
Artificial Intelligence · 2015–2025
A researcher in the Science corpus: 31 papers; trusts 865 researchers and is trusted by 33 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 678 citations into it; #6,994 of 35,345 by trust here, #1,659 by citations
- Computer Vision and Pattern Recognition — 452 citations into it; #9,478 of 21,575 by trust here, #7,499 by citations
- Reinforcement Learning — 332 citations into it; #876 of 2,242 by trust here, #1,037 by citations
- Natural Language Processing — 178 citations into it
- Control and Systems Engineering — 165 citations into it; #1,862 of 8,257 by trust here, #1,017 by citations
- Robotics & Control — 101 citations into it
- Evaluation & Benchmarks — 57 citations into it
- Safety, Robustness & Interpretability — 48 citations into it
Whom Dorsa Sadigh trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Andrew Y. Ng
- Geoffrey E. Hinton
- Diederik P. Kingma
- Pieter Abbeel
- Yoshua Bengio
- Sergey Levine
- Alex Krizhevsky
- Matthew J. Beal
- Elisabeth Köbis
- Yann LeCun
Who trusts Dorsa Sadigh overall
- Daniel S. Brown
- Siddharth Karamcheti
- Edouard Leurent
- Scott Niekum
- Felix Berkenkamp
- Hong Jun Jeon
- Anca Dragan
- Sanjit A. Seshia
- Stefano V. Albrecht
- Gokul Swamy
Papers from Dorsa Sadigh's perspective
Ordered by how strongly the researchers Dorsa Sadigh trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- Active Preference-Based Learning of Reward Functions (2017) — 477 citations
- ELLA: Exploration through Learned Language Abstraction (2021) — 64 citations
- LILA: Language-Informed Latent Actions (2021) — 42 citations
- DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset (2024) — 905 citations
- Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 (2023) — 1,105 citations
- Planning for Autonomous Cars that Leverage Effects on Human Actions (2016) — 557 citations
- OpenVLA: An Open-Source Vision-Language-Action Model (2024) — 2,794 citations
- Language to Rewards for Robotic Skill Synthesis (2023) — 422 citations
- Learning Adaptive Language Interfaces through Decomposition (2020) — 32 citations
- Multi-Agent Generative Adversarial Imitation Learning (2018) — 263 citations
- Information gathering actions over human internal state (2016) — 204 citations
- Unsupervised Visuomotor Control through Distributional Planning Networks (2019) — 42 citations
- Shared Autonomy with Learned Latent Actions (2020) — 91 citations
- Deep Local Trajectory Replanning and Control for Robot Navigation (2019) — 72 citations
- Reward Design with Language Models (2023) — 323 citations
- Controlling Assistive Robots with Learned Latent Actions (2019) — 79 citations
- Continual Adaptation for Efficient Machine Communication (2019) — 39 citations
- Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners (2023) — 392 citations
- Octo: An Open-Source Generalist Robot Policy (2024) — 1,572 citations