Suraj Nair
Artificial Intelligence · 2017–2025
A researcher in the Science corpus: 16 papers; trusts 783 researchers and is trusted by 22 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 425 citations into it; #8,490 of 35,345 by trust here, #1,674 by citations
- Computer Vision and Pattern Recognition — 334 citations into it; #7,104 of 21,575 by trust here, #4,555 by citations
- Reinforcement Learning — 255 citations into it; #740 of 2,242 by trust here, #423 by citations
- Control and Systems Engineering — 176 citations into it; #2,009 of 8,257 by trust here, #423 by citations
- Robotics & Control — 92 citations into it
- Natural Language Processing — 53 citations into it
- Representation & Self-Supervised Learning — 48 citations into it
- Biomedical Engineering — 43 citations into it
Whom Suraj Nair trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Chelsea Finn
- Sergey Levine
- Geoffrey E. Hinton
- Kaiming He
- Diederik P. Kingma
- Alex Krizhevsky
- Pieter Abbeel
- Yoshua Bengio
- Yann LeCun
- Ilya Sutskever
Who trusts Suraj Nair overall
- Chelsea Finn
- Anthony Brohan
- Moo Jin Kim
- Michael Ahn
- Ted Xiao
- Brian Ichter
- Sergey Levine
- Mohit Shridhar
- Kevin Black
- Yifeng Zhu
Papers from Suraj Nair's perspective
Ordered by how strongly the researchers Suraj Nair 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)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- RoboNet: Large-Scale Multi-Robot Learning (2019) — 437 citations
- R3M: A Universal Visual Representation for Robot Manipulation (2022) — 933 citations
- Neural Task Graphs: Generalizing to Unseen Tasks From a Single Video Demonstration (2018) — 151 citations
- Neural Task Programming: Learning to Generalize Across Hierarchical Tasks (2017) — 212 citations
- Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation (2019) — 155 citations
- Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks (2021) — 26 citations
- OpenVLA: An Open-Source Vision-Language-Action Model (2024) — 2,794 citations
- π0: A Vision-Language-Action Flow Model for General Robot Control (2024) — 2,220 citations
- Causal Induction from Visual Observations for Goal Directed Tasks (2019) — 66 citations
- Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones (2020) — 307 citations
- π0.5: a Vision-Language-Action Model with Open-World Generalization (2025) — 1,400 citations
- FAST: Efficient Action Tokenization for Vision-Language-Action Models (2025) — 551 citations
- Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models (2024) — 367 citations
- Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation (2021) — 189 citations
- Time Reversal as Self-Supervision (2018) — 12 citations