Jakob Richter

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 4 papers; trusts 15 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 117 citations into it
• Computational Theory and Mathematics — 37 citations into it
• Optimization & Training — 36 citations into it
• Statistics and Probability — 32 citations into it
• Management Science and Operations Research — 21 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Statistical Learning Theory — 17 citations into it
• Neural Architecture Search & AutoML — 16 citations into it

Whom Jakob Richter trusts overall

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

1. Frank Hutter
2. Trevor J. Hastie
3. James Bergstra
4. Yoshua Bengio
5. Max Kuhn
6. Diederik P. Kingma
7. Jasper Snoek
8. Geoffrey E. Hinton
9. Ron Kohavi
10. Mauro Birattari

Who trusts Jakob Richter overall

1. Philipp Probst
2. Bernd Bischl

Papers from Jakob Richter's perspective

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

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

Their papers

• Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data (2018) — 439 citations
• Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges (2021) — 1,013 citations
• mlr3: A modern object-oriented machine learning framework in R (2019) — 406 citations
• mlr: Machine Learning in R (2016) — 676 citations