Marcus Gualtieri

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 205 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 35 citations into it; #8,530 of 21,575 by trust here, #11,083 by citations
• Control and Systems Engineering — 26 citations into it; #1,660 of 8,257 by trust here, #1,333 by citations
• Reinforcement Learning — 22 citations into it
• Artificial Intelligence — 20 citations into it; #27,107 of 35,345 by trust here, #28,481 by citations
• Aerospace Engineering — 13 citations into it; #5,041 of 6,421 by trust here, #3,280 by citations
• Biomedical Engineering — 13 citations into it; #7,739 of 10,734 by trust here, #6,773 by citations
• Robotics & Control — 10 citations into it
• Human-Computer Interaction — 7 citations into it; #658 of 2,253 by trust here, #978 by citations

Whom Marcus Gualtieri trusts overall

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

1. Rosen Diankov
2. Yann LeCun
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. L. Eon Bottou
6. Alex Krizhevsky
7. Ross B. Girshick
8. Walter Wohlkinger
9. Andrew Zisserman
10. Diederik P. Kingma

Who trusts Marcus Gualtieri overall

1. Lucas Manuelli
2. Andreas ten Pas
3. Andy Zeng
4. Peter R. Florence
5. Robert W. Platt
6. Sergey Levine
7. Jeffrey Mahler
8. Avi Singh
9. Edward Johns
10. Andreas Eitel

Papers from Marcus Gualtieri's perspective

Ordered by how strongly the researchers Marcus Gualtieri trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• High precision grasp pose detection in dense clutter (2016) — 327 citations
• Pick and Place Without Geometric Object Models (2017) — 58 citations
• Deictic Image Maps: An Abstraction For Learning Pose Invariant Manipulation Policies (2018) — 11 citations
• Grasp Pose Detection in Point Clouds (2017) — 673 citations