Moritz Feuerpfeil

Artificial Intelligence · 2022

A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it
• Management Science and Operations Research — 10 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Statistics and Probability — 8 citations into it
• Optimization & Training — 7 citations into it

Whom Moritz Feuerpfeil trusts overall

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

1. Douglas A. Reynolds
2. Diederik P. Kingma
3. Eric R. Ziegel
4. John C. Gower
5. Donald F. Specht
6. Naomi S. Altman
7. Andreas Karlsson
8. Niko Speybroeck
9. Xuan Vinh Nguyen
10. J. Blackard

Papers from Moritz Feuerpfeil's perspective

Ordered by how strongly the researchers Moritz Feuerpfeil 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. Scikit-learn: Machine Learning in Python
5. Support-Vector Networks
6. Gradient-based learning applied to document recognition
7. Random Forests
8. Attention is All you Need (Neural Information Processing Systems)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Long Short-Term Memory

Their papers

• The Effects of Data Quality on Machine Learning Performance (2022)
• The effects of data quality on machine learning performance on tabular data (2022) — 161 citations