Lucas Manuelli

Computer Vision and Pattern Recognition · 2018–2022

A researcher in the Science corpus: 6 papers; trusts 734 researchers and is trusted by 166 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 141 citations into it; #2,867 of 21,575 by trust here, #5,236 by citations
• Artificial Intelligence — 124 citations into it; #4,877 of 35,345 by trust here, #6,796 by citations
• Control and Systems Engineering — 95 citations into it; #1,291 of 8,257 by trust here, #977 by citations
• Reinforcement Learning — 72 citations into it; #572 of 2,242 by trust here, #819 by citations
• Aerospace Engineering — 40 citations into it; #3,674 of 6,421 by trust here, #3,911 by citations
• Robotics & Control — 33 citations into it; #228 of 609 by trust here, #309 by citations
• Natural Language Processing — 28 citations into it
• Biomedical Engineering — 26 citations into it

Whom Lucas Manuelli trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Chelsea Finn
5. Sergey Levine
6. Alex Krizhevsky
7. Yann LeCun
8. Yoshua Bengio
9. Ross B. Girshick
10. Ilya Sutskever

Who trusts Lucas Manuelli overall

1. Peter R. Florence
2. Linxi (Jim) Fan
3. Chen Wang
4. Mohit Shridhar
5. Andy Zeng
6. Li Fei-Fei
7. Chelsea Finn
8. Carl Doersch
9. Wenlong Huang
10. Xingyu Liu

Papers from Lucas Manuelli's perspective

Ordered by how strongly the researchers Lucas Manuelli 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• CLIPort: What and Where Pathways for Robotic Manipulation (2021) — 963 citations
• Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation (2022) — 837 citations
• kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation (2019) — 311 citations
• Self-Supervised Correspondence in Visuomotor Policy Learning (2019) — 196 citations
• Keypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning (2020) — 113 citations
• Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation (2018) — 311 citations