Przemyslaw Biecek
Artificial Intelligence · 2017–2020
A researcher in the Science corpus: 7 papers; trusts 184 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 108 citations into it; #22,888 of 35,345 by trust here, #22,463 by citations
- Safety, Robustness & Interpretability — 53 citations into it; #2,002 of 2,251 by trust here, #2,041 by citations
- Computer Vision and Pattern Recognition — 41 citations into it; #19,566 of 21,575 by trust here, #17,258 by citations
- Statistics and Probability — 14 citations into it
- Evaluation & Benchmarks — 12 citations into it
- Natural Language Processing — 10 citations into it
- Biophysics — 8 citations into it
- AI Ethics, Fairness & Society — 6 citations into it
Whom Przemyslaw Biecek trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Erik Strumbelj
- Scott M. Lundberg
- Brandon M. Greenwell
- Marco Tulio Ribeiro
- Jerome H. Friedman
- Max Kuhn
- Igor Kononenko
- Andy Liaw
- Yihui Xie
- Bernd Bischl
Who trusts Przemyslaw Biecek overall
- Mateusz Staniak
- Christoph Molnar
- Michal Kuzba
- Evangelos Spiliotis
- Andreas Holzinger
- Alicja Gosiewska
- Giuseppe Casalicchio
- Ewa Baranowska
- Konstantinos Nikolopoulos
- Anna Saranti
Papers from Przemyslaw Biecek's perspective
Ordered by how strongly the researchers Przemyslaw Biecek trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Explainable AI Methods - A Brief Overview (2020) — 389 citations
- Do Not Trust Additive Explanations (2019) — 58 citations
- Reproducibility (2019)
- pyCeterisParibus: explaining Machine Learning models with Ceteris Paribus Profiles in Python (2019) — 16 citations
- Explanations of model predictions with live and breakDown packages (2018) — 147 citations
- DALEX: explainers for complex predictive models (2018) — 455 citations
- archivist: An R Package for Managing, Recording and Restoring Data Analysis Results (2017) — 21 citations