Oliver Kroemer
Artificial Intelligence · 2007–2020
A researcher in the Science corpus: 21 papers; trusts 527 researchers and is trusted by 49 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 263 citations into it; #5,074 of 35,345 by trust here, #5,390 by citations
- Reinforcement Learning — 247 citations into it; #1,565 of 2,242 by trust here, #1,395 by citations
- Control and Systems Engineering — 208 citations into it; #413 of 8,257 by trust here, #242 by citations
- Computer Vision and Pattern Recognition — 117 citations into it; #15,154 of 21,575 by trust here, #12,689 by citations
- Biomedical Engineering — 78 citations into it; #1,927 of 10,734 by trust here, #4,187 by citations
- Robotics & Control — 76 citations into it; #136 of 609 by trust here, #208 by citations
- Cognitive Neuroscience — 63 citations into it; #1,731 of 10,852 by trust here, #2,134 by citations
- Computational Theory and Mathematics — 29 citations into it; #11,374 of 12,182 by trust here, #4,199 by citations
Whom Oliver Kroemer trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Sergey Levine
- Stefan Schaal
- Richard S. Sutton
- Jan Peters
- Pieter Abbeel
- Andrew Barto
- Geoffrey E. Hinton
- Radford M. Neal
- Diederik P. Kingma
- Ronald J. Williams
Who trusts Oliver Kroemer overall
- Jan Peters
- Gerhard Neumann
- Herke Van Hoof
- Christian Daniel
- Sergey Levine
- Takayuki Osa
- Jason Pazis
- Jeannette Bohg
- Thomas Lampe
- Abhishek Gupta
Papers from Oliver Kroemer's perspective
Ordered by how strongly the researchers Oliver Kroemer 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
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- GENERATIVE ADVERSARIAL NETS
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Learning to select and generalize striking movements in robot table tennis (2012) — 478 citations
- Towards learning hierarchical skills for multi-phase manipulation tasks (2015) — 136 citations
- Active Reward Learning (2014) — 129 citations
- A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (2019) — 509 citations
- Active reward learning with a novel acquisition function (2015) — 69 citations
- Combining active learning and reactive control for robot grasping (2010) — 173 citations
- Learning grasp affordance densities (2011) — 95 citations
- Towards Motor Skill Learning for Robotics (2007) — 40 citations
- Graph-Structured Visual Imitation (2019) — 79 citations
- Interaction primitives for human-robot cooperation tasks (2014) — 233 citations
- Active learning using mean shift optimization for robot grasping (2009) — 34 citations
- Learning Visual Representations for Interactive Systems (2009) — 11 citations
- Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation (2020) — 32 citations
- Movement templates for learning of hitting and batting (2010) — 181 citations
- Generalization of human grasping for multi-fingered robot hands (2012) — 118 citations
- A Review of Tactile Information: Perception and Action Through Touch (2020) — 308 citations
- Learning Semantic Embedding Spaces for Slicing Vegetables (2019) — 15 citations
- Regrasping Using Tactile Perception and Supervised Policy Learning (2017) — 11 citations
- BiGS: BioTac Grasp Stability Dataset (2016) — 33 citations
- A Non-Parametric Approach to Dynamic Programming (2011) — 20 citations