Marius Thomas Lindauer

Artificial Intelligence · 2011–2021

A researcher in the Science corpus: 9 papers; trusts 190 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 261 citations into it; #7,525 of 35,345 by trust here, #15,004 by citations
• Computational Theory and Mathematics — 100 citations into it; #4,221 of 12,182 by trust here, #5,954 by citations
• Optimization & Training — 87 citations into it
• Neural Architecture Search & AutoML — 71 citations into it; #89 of 283 by trust here, #230 by citations
• Computer Vision and Pattern Recognition — 62 citations into it
• Management Science and Operations Research — 44 citations into it
• Computer Networks and Communications — 43 citations into it; #2,489 of 9,886 by trust here, #5,133 by citations
• Evaluation & Benchmarks — 32 citations into it

Whom Marius Thomas Lindauer trusts overall

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

1. Frank Hutter
2. Holger H. Hoos
3. Kevin Leyton-Brown
4. Katharina Eggensperger
5. Yoshua Bengio
6. Geoffrey E. Hinton
7. James Bergstra
8. Alex Krizhevsky
9. Lin Xu
10. Aaron Klein

Who trusts Marius Thomas Lindauer overall

1. Colin White
2. Marc-André Zöller
3. Bernd Bischl
4. Marco F. Huber
5. Sam Nolen
6. Yash Savani
7. Yoshua Bengio
8. Shuhei Watanabe
9. Antoine Yang
10. Willie Neiswanger

Papers from Marius Thomas Lindauer's perspective

Ordered by how strongly the researchers Marius Thomas Lindauer 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization (2021) — 526 citations
• Warmstarting of Model-based Algorithm Configuration (2017) — 70 citations
• Best Practices for Scientific Research on Neural Architecture Search (2019) — 155 citations
• Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates (2017) — 7 citations
• Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges (2021) — 1,013 citations
• Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning (2020) — 404 citations
• Practical Automated Machine Learning for the AutoML Challenge 2018 (2018)
• Efficient Parameter Importance Analysis via Ablation with Surrogates (2017) — 59 citations
• Potassco: The Potsdam Answer Set Solving Collection (2011) — 594 citations