We have a new paper titled “Deep learning-based analysis of true triaxial DEM simulations: Role of fabric and particle aspect ratio”, which will appear in Computers and Geotechnics. Thank you for Nazanin, Merita, Mohammad, and Torsten in Bochum and Pegah in Hamburg for this wonderful interdisciplinary collaboration! 🏗🏢🌎
Layer Sparsity
Our paper “Layer sparsity in neural networks” has now been accepted at J. Stat. Plan. Inference. Congrats to Mahsa, and many thanks to our great collaborator Mohamed! 🗼
Anomaly detection
Our new paper “AnomalyDINO: boosting patch-based few-shot anomaly detection with DINOv2” is now on arXiv. Thanks for the great efforts, Simon, Mike, and Asja! 👷♀️🏗👷♂️
Single-Model Attribution at ICML
Our paper “Single-Model Attribution of Generative Models Through Final-Layer Inversion” has now been accepted at ICML. Well done, Mike and Jonas, and thanks for the great collaboration, Asja! 🍻
German Podcast
In unserem neuen Podcast “Data Science Talks” erklären wir aktuelle Entwicklungen in Data Science und künstlicher Intelligenz. Beispielsweise zeigen wir mit Gregor Kasieczka die Bedeutung von Data Science in der Teilchenphysik auf, diskutieren mit Judith Möller die Auswirkungen von KI in den sozialen Medien, und beleuchten mit Eva Bittner die Zusammenarbeit zwischen Mensch und Maschine.
Die ersten Folgen werden in Kürze online sein. Aus aktuellem Anlass gibt es schon diese Woche einen kurzen Teaser zum Nobelpreisträger Peter Higgs.
Fairness in deep generative models II
Our paper “Benchmarking the fairness of image upsampling methods” is now accepted at ACM FAccT 2024. Congratulations everyone! 🇩🇪🇨🇭🏴☠️
Brief Interview
You can find a brief interview (in German) with Johannes about his research here.
Fairness in deep generative models
Our new paper “Benchmarking the fairness of image upsampling methods” is now on arXiv. Awesome job, Mike—and thanks Imant, Julia, and Asja for the great collaboration! 🇩🇪🇨🇭🏴☠️
Change points in time series
Our discussion paper in JRSSB about “Probabilistic and Statistical Aspects of Machine Learning” is available online. Congratulations, Ayla! 🇬🇧
Affine Invariance in Neural Networks
Our new paper “Affine invariance in continuous-domain convolutional neural networks” is now on arXiv. Well done, Ali! 🖥
Johannes Lederer 