Marc-André Zöller

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 337 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 200 citations into it; #25,237 of 35,345 by trust here, #21,316 by citations
• Computational Theory and Mathematics — 55 citations into it
• Optimization & Training — 52 citations into it
• Neural Architecture Search & AutoML — 47 citations into it; #84 of 283 by trust here, #164 by citations
• Management Science and Operations Research — 45 citations into it
• Computer Vision and Pattern Recognition — 38 citations into it
• Evaluation & Benchmarks — 29 citations into it; #2,259 of 3,147 by trust here, #2,482 by citations
• Information Systems — 22 citations into it

Whom Marc-André Zöller trusts overall

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

1. Frank Hutter
2. L. Eon Bottou
3. Matthias Feurer
4. James Bergstra
5. Alex Krizhevsky
6. Yoshua Bengio
7. Geoffrey E. Hinton
8. Isabelle M Guyon
9. Martin Wistuba
10. Katharina Eggensperger

Who trusts Marc-André Zöller overall

1. Maryam Ashoori
2. Yufan Jiang
3. Li Yang
4. Xin He
5. Tien-Dung Nguyen
6. Nick Erickson
7. Justin D. Weisz
8. Bernd Bischl
9. Shuangzhi Wu
10. Abdallah Shami

Papers from Marc-André Zöller's perspective

Ordered by how strongly the researchers Marc-André Zöller 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. 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. 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

• AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model (2020) — 12 citations
• Benchmark and Survey of Automated Machine Learning Frameworks (2019) — 449 citations
• Survey on Automated Machine Learning (2019) — 89 citations