Simon Bartels

Artificial Intelligence · 2015–2017

A researcher in the Science corpus: 3 papers; trusts 54 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 46 citations into it; #12,358 of 35,345 by trust here, #12,580 by citations
• Optimization & Training — 44 citations into it; #1,387 of 2,601 by trust here, #1,280 by citations
• Probabilistic & Bayesian Methods — 25 citations into it
• Management Science and Operations Research — 19 citations into it; #4,161 of 11,946 by trust here, #2,603 by citations
• Computational Theory and Mathematics — 18 citations into it; #8,940 of 12,182 by trust here, #9,306 by citations
• Neural Architecture Search & AutoML — 18 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Evaluation & Benchmarks — 8 citations into it

Whom Simon Bartels trusts overall

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

1. Jasper Snoek
2. Alex Krizhevsky
3. James Bergstra
4. Kevin Swersky
5. Geoffrey E. Hinton
6. Frank Hutter
7. Yoshua Bengio
8. Diederik P. Kingma
9. Radford M. Neal
10. Ryan P. Adams

Who trusts Simon Bartels overall

1. Kirthevasan Kandasamy
2. Aaron Klein
3. Stefan Falkner
4. Liam Li
5. Frank Hutter
6. Peter I. Frazier
7. Gautam Dasarathy
8. Lisha Li
9. Kevin G. Jamieson
10. Abhishek Gupta

Papers from Simon Bartels's perspective

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

Their papers

• Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets (2016) — 620 citations
• Fast Bayesian hyperparameter optimization on large datasets (2017) — 67 citations
• Towards efficient Bayesian Optimization for Big Data (2015)