Clemens Eppner
Control and Systems Engineering · 2016–2020
A researcher in the Science corpus: 7 papers; trusts 208 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Control and Systems Engineering — 78 citations into it; #922 of 8,257 by trust here, #607 by citations
- Computer Vision and Pattern Recognition — 69 citations into it; #6,565 of 21,575 by trust here, #8,021 by citations
- Biomedical Engineering — 43 citations into it; #3,422 of 10,734 by trust here, #4,405 by citations
- Artificial Intelligence — 41 citations into it; #33,193 of 35,345 by trust here, #26,682 by citations
- Reinforcement Learning — 34 citations into it; #927 of 2,242 by trust here, #1,190 by citations
- Aerospace Engineering — 32 citations into it; #5,261 of 6,421 by trust here, #2,734 by citations
- Robotics & Control — 27 citations into it; #341 of 609 by trust here, #371 by citations
- Computational Mechanics — 12 citations into it
Whom Clemens Eppner trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Sergey Levine
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Sebastian Thrun
- Ross B. Girshick
- Kaiming He
- Yoshua Bengio
- Pieter Abbeel
- Jan Peters
Who trusts Clemens Eppner overall
- Andy Zeng
- Mohit Shridhar
- Arsalan Mousavian
- Vikash Kumar
- Christopher Xie
- Yu Xiang
- Priyanka Mandikal
- Dieter Fox
- Martin Sundermeyer
- Jeffrey Mahler
Papers from Clemens Eppner's perspective
Ordered by how strongly the researchers Clemens Eppner trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Et al
Their papers
- 6-DOF GraspNet: Variational Grasp Generation for Object Manipulation (2019) — 695 citations
- 6-DOF Grasping for Target-driven Object Manipulation in Clutter (2019) — 238 citations
- Combining model-based policy search with online model learning for control of physical humanoids (2016) — 57 citations
- Alternative Paths Planner (APP) for Provably Fixed-time Manipulation Planning in Semi-structured Environments (2020) — 8 citations
- Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems (2016) — 202 citations
- Learning dexterous manipulation for a soft robotic hand from human demonstrations (2016) — 197 citations
- Self-supervised 6D Object Pose Estimation for Robot Manipulation (2019) — 231 citations