Stefan Depeweg

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 7 papers; trusts 56 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it; #16,244 of 35,345 by trust here, #17,412 by citations
• Reinforcement Learning — 47 citations into it; #806 of 2,242 by trust here, #1,331 by citations
• Probabilistic & Bayesian Methods — 27 citations into it; #561 of 1,525 by trust here, #543 by citations
• Control and Systems Engineering — 17 citations into it; #5,168 of 8,257 by trust here, #4,722 by citations
• Management Science and Operations Research — 14 citations into it
• Computational Theory and Mathematics — 11 citations into it
• Safety, Robustness & Interpretability — 11 citations into it
• Cognitive Neuroscience — 9 citations into it

Whom Stefan Depeweg trusts overall

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

1. José Miguel Hernández-Lobato
2. David John Cameron MacKay
3. J. Urgen Schmidhuber
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Javier García
7. Jan Peters
8. Alex Kendall
9. Charles Blundell
10. Yarin Gal

Who trusts Stefan Depeweg overall

1. Lucy R. Chai
2. Javier Antorán Cabiscol
3. José Miguel Hernández-Lobato
4. Steffen Udluft
5. Daniel Hein
6. Yarin Gal
7. Yingzhen Li
8. Phillip Swazinna
9. Javier Antorán
10. Thomas A. Runkler

Papers from Stefan Depeweg's perspective

Ordered by how strongly the researchers Stefan Depeweg 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. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables (2017) — 46 citations
• A benchmark environment motivated by industrial control problems (2017) — 68 citations
• Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks (2016) — 168 citations
• Modeling Epistemic and Aleatoric Uncertainty with Bayesian Neural Networks and Latent Variables (2019) — 28 citations
• Sensitivity analysis for predictive uncertainty (2017) — 8 citations
• Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems (2017) — 27 citations
• Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning (2017) — 521 citations