Nicolas Schilling

Artificial Intelligence · 2014–2017

A researcher in the Science corpus: 5 papers; trusts 91 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #7,358 of 35,345 by trust here, #12,387 by citations
• Optimization & Training — 32 citations into it
• Computational Theory and Mathematics — 17 citations into it; #2,172 of 12,182 by trust here, #2,931 by citations
• Neural Architecture Search & AutoML — 12 citations into it
• Signal Processing — 11 citations into it; #6,550 of 7,572 by trust here, #6,355 by citations
• Probabilistic & Bayesian Methods — 10 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Management Science and Operations Research — 9 citations into it

Whom Nicolas Schilling trusts overall

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

1. Frank Hutter
2. James Bergstra
3. Yoshua Bengio
4. Jasper Snoek
5. Geoffrey E. Hinton
6. Matthias Feurer
7. Hugo Larochelle
8. Rémi Bardenet
9. Chih-Jen Lin
10. Alex Krizhevsky

Who trusts Nicolas Schilling overall

1. Marc-André Zöller
2. Martin Wistuba
3. Marco F. Huber
4. Matthias Feurer
5. Gang Luo
6. Katharina Eggensperger
7. Frank Hutter
8. Jan N. van Rijn
9. Marius Thomas Lindauer
10. Lars Schmidt-Thieme

Papers from Nicolas Schilling's perspective

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

Their papers

• Automatic Frankensteining: Creating Complex Ensembles Autonomously (2017) — 33 citations
• Learning hyperparameter optimization initializations (2015) — 108 citations
• Hyperparameter Search Space Pruning - A New Component for Sequential Model-Based Hyperparameter Optimization (2015) — 78 citations
• Scalable Gaussian process-based transfer surrogates for hyperparameter optimization (2017) — 150 citations
• Learning time-series shapelets (2014) — 491 citations