Philipp Probst

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 4 papers; trusts 48 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 56 citations into it; #11,138 of 35,345 by trust here, #13,544 by citations
• Statistics and Probability — 28 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Statistical Learning Theory — 12 citations into it
• Molecular Biology — 11 citations into it; #12,567 of 17,773 by trust here, #14,176 by citations
• Information Systems — 9 citations into it; #5,119 of 8,771 by trust here, #6,195 by citations
• Computational Theory and Mathematics — 8 citations into it; #5,675 of 12,182 by trust here, #5,465 by citations

Whom Philipp Probst trusts overall

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

1. Bernd Bischl
2. Frank Hutter
3. Torsten Hothorn
4. Michel Lang
5. Andy Liaw
6. Jerome H. Friedman
7. Giuseppe Casalicchio
8. Trevor J. Hastie
9. Marvin N. Wright
10. Holger H. Hoos

Who trusts Philipp Probst overall

1. Marc-André Zöller
2. Daniel Mesafint Belete
3. Hristos Tyralis
4. Han-Jia Ye
5. Bernd Bischl
6. Feng Xie
7. R. Couronné
8. Anne-Laure Boulesteix
9. Marco F. Huber
10. Georgia A. Papacharalampous

Papers from Philipp Probst's perspective

Ordered by how strongly the researchers Philipp Probst trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Learning Multiple Layers of Features from Tiny Images
4. Gradient-based learning applied to document recognition
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Random Forests
9. Long Short-Term Memory
10. Practical Bayesian Optimization of Machine Learning Algorithms

Their papers

• Tunability: Importance of Hyperparameters of Machine Learning Algorithms (2018) — 816 citations
• Hyperparameters and tuning strategies for random forest (2018) — 2,058 citations
• Random forest versus logistic regression: a large-scale benchmark experiment (2018) — 785 citations
• To tune or not to tune the number of trees in random forest? (2017) — 460 citations