Matthias Feurer

Artificial Intelligence · 2014–2021

A researcher in the Science corpus: 12 papers; trusts 144 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 232 citations into it; #6,187 of 35,345 by trust here, #9,170 by citations
• Optimization & Training — 84 citations into it; #428 of 2,601 by trust here, #355 by citations
• Computational Theory and Mathematics — 57 citations into it; #1,371 of 12,182 by trust here, #1,813 by citations
• Evaluation & Benchmarks — 54 citations into it; #2,622 of 3,147 by trust here, #2,938 by citations
• Neural Architecture Search & AutoML — 52 citations into it; #192 of 283 by trust here, #77 by citations
• Management Science and Operations Research — 45 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it
• ML Systems & Hardware — 31 citations into it

Whom Matthias Feurer trusts overall

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

1. Frank Hutter
2. James Bergstra
3. Jasper Snoek
4. Holger H. Hoos
5. Yoshua Bengio
6. Katharina Eggensperger
7. Chris J. Thornton
8. Kevin Leyton-Brown
9. Geoffrey E. Hinton
10. Kevin Swersky

Who trusts Matthias Feurer overall

1. Marc-André Zöller
2. Frank Hutter
3. Martin Wistuba
4. Marco F. Huber
5. Katharina Eggensperger
6. Stefan Falkner
7. Gang Luo
8. Marius Thomas Lindauer
9. Nick Erickson
10. Jan N. van Rijn

Papers from Matthias Feurer's perspective

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

Their papers

• Initializing Bayesian Hyperparameter Optimization via Meta-Learning (2015) — 492 citations
• SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization (2021) — 526 citations
• Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning (2020) — 404 citations
• Auto-sklearn: Efficient and Robust Automated Machine Learning (2019) — 354 citations
• Practical Automated Machine Learning for the AutoML Challenge 2018 (2018)
• Towards Further Automation in AutoML (2018)
• OpenML Benchmarking Suites and the OpenML100 (2017) — 77 citations
• OpenML Benchmarking Suites (2017) — 232 citations
• Towards Automatically-Tuned Neural Networks (2016) — 222 citations
• Efficient and Robust Automated Machine Learning (2015) — 1,941 citations
• Efficient and Robust Automated Machine Learning (2015)
• Using Meta-Learning to Initialize Bayesian Optimization of Hyperparameters (2014) — 79 citations