18 citations · 24 across the 7 of their papers we have counts for
3 papers · 1 filter
On the Challenges and Opportunities in Generative AI
Laura Manduchi, Clara Meister, Kushagra Pandey +23
The field of deep generative modeling has grown rapidly in the last few years. With the availability of massive amounts of training data coupled with advances in scalable unsupervi…
Training Normalizing Flows from Dependent Data
Matthias Kirchler, Christoph Lippert, Marius Kloft
Normalizing flows are powerful non-parametric statistical models that function as a hybrid between density estimators and generative models. Current learning algorithms for normali…
Explainability Requires Interactivity
Matthias Kirchler, Martin Graf, Marius Kloft +1
When explaining the decisions of deep neural networks, simple stories are tempting but dangerous. Especially in computer vision, the most popular explanation approaches give a fals…