18 citations · 24 across the 7 of their papers we have counts for
7 papers
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…
MixerFlow: MLP-Mixer meets Normalising Flows
Eshant English, Matthias Kirchler, Christoph Lippert
Normalising flows are generative models that transform a complex density into a simpler density through the use of bijective transformations enabling both density estimation and da…
Kernelised Normalising Flows
Eshant English, Matthias Kirchler, Christoph Lippert
Normalising Flows are non-parametric statistical models characterised by their dual capabilities of density estimation and generation. This duality requires an inherently invertibl…
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…
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics
Aiham Taleb, Matthias Kirchler, Remo Monti +1
High annotation costs are a substantial bottleneck in applying modern deep learning architectures to clinically relevant medical use cases, substantiating the need for novel algori…
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…
Two-sample Testing Using Deep Learning
Matthias Kirchler, Shahryar Khorasani, Marius Kloft +1
We propose a two-sample testing procedure based on learned deep neural network representations. To this end, we define two test statistics that perform an asymptotic location test…