Markus Wulfmeier

Artificial Intelligence · 2015–2021

A researcher in the Science corpus: 7 papers; trusts 222 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 159 citations into it; #9,248 of 35,345 by trust here, #6,720 by citations
• Reinforcement Learning — 147 citations into it; #1,168 of 2,242 by trust here, #762 by citations
• Computer Vision and Pattern Recognition — 73 citations into it; #13,588 of 21,575 by trust here, #14,478 by citations
• Control and Systems Engineering — 46 citations into it; #2,944 of 8,257 by trust here, #673 by citations
• Computational Theory and Mathematics — 22 citations into it
• Biomedical Engineering — 20 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Robotics & Control — 20 citations into it

Whom Markus Wulfmeier trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Sergey Levine
4. Yann LeCun
5. Diederik P. Kingma
6. Pieter Abbeel
7. Alex Krizhevsky
8. Ilya Sutskever
9. Richard S. Sutton
10. Christian Szegedy

Who trusts Markus Wulfmeier overall

1. Lei Tai
2. Sergey Levine
3. Kelvin Xu
4. Garrett Wilson
5. Yu Kong
6. Chelsea Finn
7. Zhengyao Jiang
8. Hao Wang
9. Jiachen Yang
10. Sammy Joe Christen

Papers from Markus Wulfmeier's perspective

Ordered by how strongly the researchers Markus Wulfmeier 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Reverse Curriculum Generation for Reinforcement Learning (2017) — 535 citations
• Maximum Entropy Deep Inverse Reinforcement Learning (2015) — 458 citations
• Mutual Alignment Transfer Learning (2017) — 63 citations
• Incremental Adversarial Domain Adaptation for Continually Changing Environments (2017) — 138 citations
• From Motor Control to Team Play in Simulated Humanoid Football (2021) — 163 citations
• Regularized Hierarchical Policies for Compositional Transfer in Robotics (2019) — 27 citations
• Watch this: Scalable cost-function learning for path planning in urban environments (2016) — 120 citations