Mark Pfeiffer

Computer Vision and Pattern Recognition · 2015–2018

A researcher in the Science corpus: 4 papers; trusts 12 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 33 citations into it; #15,401 of 21,575 by trust here, #11,498 by citations
• Artificial Intelligence — 18 citations into it
• Reinforcement Learning — 17 citations into it
• Control and Systems Engineering — 16 citations into it; #7,181 of 8,257 by trust here, #5,227 by citations
• Robotics & Control — 13 citations into it
• Optimization & Training — 5 citations into it

Whom Mark Pfeiffer trusts overall

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

1. Pieter Abbeel
2. Yann LeCun
3. Diederik P. Kingma
4. Kaiming He
5. Steven M. LaValle
6. Geoffrey E. Hinton
7. Morgan Quigley
8. Andrew Y. Ng
9. Yoshua Bengio
10. Alex Krizhevsky

Who trusts Mark Pfeiffer overall

1. Lei Tai
2. Edouard Leurent
3. Bo Liu
4. Wilko Schwarting
5. Tingxiang Fan
6. Pinxin Long
7. Kai Zhu
8. Scott Fujimoto
9. Anirudh Vemula
10. Jingwei Zhang

Papers from Mark Pfeiffer's perspective

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

Their papers

• From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots (2016) — 428 citations
• Predicting actions to act predictably: Cooperative partial motion planning with maximum entropy models (2016) — 108 citations
• Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Mapless Navigation by Leveraging Prior Demonstrations (2018) — 184 citations
• Kinematic and dynamic vehicle models for autonomous driving control design (2015) — 789 citations