Rico Jonschkowski

Computer Vision and Pattern Recognition · 2014–2021

A researcher in the Science corpus: 11 papers; trusts 546 researchers and is trusted by 52 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 166 citations into it; #12,553 of 21,575 by trust here, #7,499 by citations
• Artificial Intelligence — 159 citations into it; #4,193 of 35,345 by trust here, #7,858 by citations
• Reinforcement Learning — 84 citations into it; #1,128 of 2,242 by trust here, #1,155 by citations
• Representation & Self-Supervised Learning — 63 citations into it
• Control and Systems Engineering — 49 citations into it; #2,406 of 8,257 by trust here, #1,038 by citations
• Aerospace Engineering — 36 citations into it
• Model Architectures — 30 citations into it
• Optimization & Training — 21 citations into it

Whom Rico Jonschkowski trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Sergey Levine
5. Richard S. Sutton
6. Yann LeCun
7. Martin A. Riedmiller
8. Laurenz Wiskott
9. Diederik P. Kingma
10. Alex Graves

Who trusts Rico Jonschkowski overall

1. Antonin Raffin
2. Timothée Lesort
3. Sergey Levine
4. Chelsea Finn
5. Debidatta Dwibedi
6. Oliver Brock
7. Natalia Díaz Rodríguez
8. Oliver Kroemer
9. Ashley Hill
10. Dibya Ghosh

Papers from Rico Jonschkowski's perspective

Ordered by how strongly the researchers Rico Jonschkowski 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale (2021) — 331 citations
• Learning state representations with robotic priors (2015) — 199 citations
• Depth From Videos in the Wild: Unsupervised Monocular Depth Learning From Unknown Cameras (2019) — 399 citations
• PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations (2017) — 68 citations
• State Representation Learning in Robotics: Using Prior Knowledge about Physical Interaction (2014) — 55 citations
• KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects (2019) — 145 citations
• Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems (2016) — 202 citations
• Conditional Object-Centric Learning from Video (2021) — 314 citations
• Contextual Learning (2020) — 69 citations
• Unsupervised Learning of State Representations for Multiple Tasks (2017) — 9 citations
• End-to-End Learnable Histogram Filters (2017) — 41 citations