Jonathan Tremblay

Computer Vision and Pattern Recognition · 2018–2022

A researcher in the Science corpus: 6 papers; trusts 291 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 189 citations into it; #14,714 of 21,575 by trust here, #3,713 by citations
• Artificial Intelligence — 64 citations into it
• Computer Graphics and Computer-Aided Design — 43 citations into it
• Control and Systems Engineering — 42 citations into it; #6,066 of 8,257 by trust here, #2,213 by citations
• Reinforcement Learning — 30 citations into it
• Aerospace Engineering — 28 citations into it
• Robotics & Control — 16 citations into it
• LLM Agents & Tool Use — 6 citations into it

Whom Jonathan Tremblay trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Ross B. Girshick
6. Christian Szegedy
7. Yoshua Bengio
8. Yann LeCun
9. Shaoqing Ren
10. Karen Simonyan

Who trusts Jonathan Tremblay overall

1. Sergey I. Nikolenko
2. Yu Xiang
3. Gu Wang
4. Priyanka Mandikal
5. Christopher Xie
6. Lucas Manuelli
7. Oliver Kroemer
8. Jean-Philippe Mercier
9. Sulabh Kumra
10. Peter R. Florence

Papers from Jonathan Tremblay's perspective

Ordered by how strongly the researchers Jonathan Tremblay 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• Efficient Geometry-aware 3D Generative Adversarial Networks (2021) — 1,520 citations
• ProgPrompt: Generating Situated Robot Task Plans using Large Language Models (2022) — 1,032 citations
• Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization (2018) — 1,009 citations
• Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects (2018) — 755 citations
• DexYCB: A Benchmark for Capturing Hand Grasping of Objects (2021) — 463 citations
• Hierarchical Planning for Long-Horizon Manipulation with Geometric and Symbolic Scene Graphs (2020) — 153 citations