Jürgen Leitner

Computer Vision and Pattern Recognition · 2013–2019

A researcher in the Science corpus: 12 papers; trusts 395 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 156 citations into it; #12,481 of 21,575 by trust here, #6,737 by citations
• Artificial Intelligence — 131 citations into it; #22,274 of 35,345 by trust here, #10,667 by citations
• Control and Systems Engineering — 94 citations into it; #1,989 of 8,257 by trust here, #1,038 by citations
• Reinforcement Learning — 67 citations into it
• Aerospace Engineering — 59 citations into it
• Biomedical Engineering — 42 citations into it; #4,038 of 10,734 by trust here, #6,932 by citations
• Robotics & Control — 35 citations into it; #517 of 609 by trust here, #395 by citations
• Representation & Self-Supervised Learning — 29 citations into it

Whom Jürgen Leitner trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Richard S. Sutton
5. Andrew Zisserman
6. J. Urgen Schmidhuber
7. Yoshua Bengio
8. Ilya Sutskever
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Jürgen Leitner overall

1. Lei Tai
2. Douglas Morrison
3. Peter Corke
4. Stephen James
5. Haoshu Fang
6. Andrew Hundt
7. Yongming Rao
8. Fangyi Zhang
9. Oliver Kroemer
10. Jonathan Tremblay

Papers from Jürgen Leitner's perspective

Ordered by how strongly the researchers Jürgen Leitner 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control (2015) — 289 citations
• Cartman: The Low-Cost Cartesian Manipulator that Won the Amazon Robotics Challenge (2017) — 147 citations
• The limits and potentials of deep learning for robotics (2018) — 566 citations
• Artificial neural networks for spatial perception: Towards visual object localisation in humanoid robots (2013) — 9 citations
• Vision-Based Reaching Using Modular Deep Networks: from Simulation to the Real World (2016) — 18 citations
• Curiosity driven reinforcement learning for motion planning on humanoids (2014) — 95 citations
• Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach (2018) — 649 citations
• Learning robust, real-time, reactive robotic grasping (2019) — 474 citations
• Benchmarking Simulated Robotic Manipulation Through a Real World Dataset (2019) — 32 citations
• Sim-to-real Transfer of Visuo-motor Policies for Reaching in Clutter: Domain Randomization and Adaptation with Modular Networks (2017) — 38 citations
• Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies (2016) — 42 citations
• The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research (2016) — 83 citations