Caelan Reed Garrett

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 28 citations into it; #21,439 of 35,345 by trust here, #20,377 by citations
• Computer Vision and Pattern Recognition — 22 citations into it; #13,321 of 21,575 by trust here, #13,542 by citations
• Control and Systems Engineering — 13 citations into it
• Reinforcement Learning — 13 citations into it
• Robotics & Control — 12 citations into it
• Computer Networks and Communications — 8 citations into it
• Aerospace Engineering — 7 citations into it
• LLM Agents & Tool Use — 5 citations into it

Whom Caelan Reed Garrett trusts overall

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

1. Leslie Pack Kaelbling
2. Rosen Diankov
3. Malte Helmert
4. Maria Fox
5. Nils J. Nilsson
6. Steven M. LaValle
7. Tomas Lozano-Perez
8. James J. Kuffner Jr.
9. Blai Bonet
10. R´emi Munos

Who trusts Caelan Reed Garrett overall

1. Kevin Qinghong Lin
2. Tom Silver
3. Michael Janner
4. Yifeng Zhu
5. Rohan Chitnis
6. Christopher Agia
7. Tomas Lozano-Perez
8. Josh Tenenbaum
9. Toki Migimatsu
10. Wenlong Huang

Papers from Caelan Reed Garrett's perspective

Ordered by how strongly the researchers Caelan Reed Garrett 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. Reinforcement Learning: An Introduction
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Integrated Task and Motion Planning (2020) — 718 citations
• PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning (2018) — 353 citations
• Online Replanning in Belief Space for Partially Observable Task and Motion Problems (2019) — 145 citations