Steffen Grünewälder
Artificial Intelligence · 2009–2013
A researcher in the Science corpus: 6 papers; trusts 52 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 51 citations into it; #12,850 of 35,345 by trust here, #16,166 by citations
- Reinforcement Learning — 44 citations into it; #907 of 2,242 by trust here, #1,344 by citations
- Statistical Learning Theory — 29 citations into it
- Probabilistic & Bayesian Methods — 24 citations into it; #782 of 1,525 by trust here, #817 by citations
- Computer Vision and Pattern Recognition — 16 citations into it
- Representation & Self-Supervised Learning — 16 citations into it; #1,604 of 3,145 by trust here, #2,217 by citations
- Control and Systems Engineering — 15 citations into it
- Statistics and Probability — 12 citations into it; #5,819 of 12,932 by trust here, #5,661 by citations
Whom Steffen Grünewälder trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Le Song
- Kenji Fukumizu
- Arthur Gretton
- Richard S. Sutton
- Alex Smola
- Dimitri P. Bertsekas
- Satinder Singh
- Bernhard Schölkopf
- Andrew Barto
- Geoffrey E. Hinton
Who trusts Steffen Grünewälder overall
- Krikamol Muandet
- Kenji Fukumizu
- Arthur Gretton
- Yu Nishiyama
- Le Song
- Nan Jiang
- Herke Van Hoof
- Bo Dai
- Bharath K. Sriperumbudur
- Byron Boots
Papers from Steffen Grünewälder's perspective
Ordered by how strongly the researchers Steffen Grünewälder 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Reinforcement Learning: An Introduction
Their papers
- Modelling transition dynamics in MDPs with RKHS embeddings (2012) — 140 citations
- The optimal unbiased value estimator and its relation to LSTD, TD and MC (2009) — 7 citations
- Conditional mean embeddings as regressors (2012) — 165 citations
- Smooth Operators (2013) — 39 citations
- Construction of approximation spaces for reinforcement learning (2013) — 23 citations
- Regret Bounds for Gaussian Process Bandit Problems (2010) — 101 citations