02.01.2019
©Joachim Wendler - stock-adobe.com
Five Papers in 2019 Highlight Scientific Impact
We are happy to announce that MCML researchers are represented in 2019 with five papers in highly-ranked journals. Congrats to our researchers!
Essential guidelines for computational method benchmarking.
Genome Biology 20.125. Jun. 2019. DOI
Tunability: Importance of Hyperparameters of Machine Learning Algorithms.
Journal of Machine Learning Research 20. Mar. 2019. PDF
Current best practices in single‐cell RNA‐seq analysis: a tutorial.
Molecular Systems Biology 15.e8746. Jun. 2019. DOI GitHub
scGen predicts single-cell perturbation responses.
Nature Methods 16.8. Jul. 2019. DOI GitHub
scSLAM-seq reveals core features of transcription dynamics in single cells.
Nature 571. Jul. 2019. DOI
Related
15.01.2026
Blind Matching – Aligning Images and Text Without Training or Labels
CVPR 2025 research from Daniel Cremers’ group shows how images and text can be aligned without training data, labels, or paired examples.
08.01.2026
High-Res Images, Less Wait: A Simple Flow for Image Generation
ECCV 2024 research led by Björn Ommer’s team enables faster high-resolution image generation by boosting diffusion models with flow matching.
©Joachim Wendler - stock-adobe.com
02.01.2026
MCML Researchers in Highly-Ranked Journals
We are excited to announce that MCML researchers have four papers published in highly-ranked journals in 2026.
18.12.2025
"See, Don’t Assume": Revealing and Reducing Gender Bias in AI
ICLR 2025 research led by Zeynep Akata’s team reveals and reduces gender bias in popular vision-language AI models.
