Marco F. Huber

Artificial Intelligence · 2008–2020

A researcher in the Science corpus: 5 papers; trusts 646 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 322 citations into it; #10,831 of 35,345 by trust here, #13,760 by citations
• Safety, Robustness & Interpretability — 76 citations into it
• Computational Theory and Mathematics — 64 citations into it
• Computer Vision and Pattern Recognition — 60 citations into it
• Optimization & Training — 55 citations into it
• Management Science and Operations Research — 50 citations into it
• Neural Architecture Search & AutoML — 45 citations into it; #82 of 283 by trust here, #164 by citations
• Information Systems — 43 citations into it

Whom Marco F. Huber trusts overall

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

1. Frank Hutter
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. L. Eon Bottou
6. James Bergstra
7. Diederik P. Kingma
8. Leo Breiman
9. Ilya Sutskever
10. Yann LeCun

Who trusts Marco F. Huber overall

1. Samuel J. Gershman
2. Maryam Ashoori
3. Yufan Jiang
4. Li Yang
5. Gesina Schwalbe
6. Xin He
7. Yu Nishiyama
8. Marc Peter Deisenroth
9. Jonathan Ko
10. Tien-Dung Nguyen

Papers from Marco F. Huber's perspective

Ordered by how strongly the researchers Marco F. Huber 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. Going deeper with convolutions

Their papers

• Analytic moment-based Gaussian process filtering (2009) — 162 citations
• On entropy approximation for Gaussian mixture random vectors (2008) — 323 citations
• A Survey on the Explainability of Supervised Machine Learning (2020) — 965 citations
• Benchmark and Survey of Automated Machine Learning Frameworks (2019) — 449 citations
• Survey on Automated Machine Learning (2019) — 89 citations