4 citations · 13 across the 7 of their papers we have counts for
8 papers
MRExtrap: Longitudinal Aging of Brain MRIs using Linear Modeling in Latent Space
Jaivardhan Kapoor, Jakob H. Macke, Christian F. Baumgartner
Simulating aging in 3D brain MRI scans can reveal disease progression patterns in neurological disorders such as Alzheimer's disease. Current deep learning-based generative models…
EDAPT: Towards Calibration-Free BCIs with Continual Online Adaptation
Lisa Haxel, Jaivardhan Kapoor, Ulf Ziemann +1
Brain-computer interfaces (BCIs) suffer from accuracy degradation as neural signals drift over time and vary across users, requiring frequent recalibration that limits practical de…
sbi reloaded: a toolkit for simulation-based inference workflows
Jan Boelts, Michael Deistler, Manuel Gloeckler +30
Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a…
Latent Diffusion for Neural Spiking Data
Jaivardhan Kapoor, Auguste Schulz, Julius Vetter +3
Modern datasets in neuroscience enable unprecedented inquiries into the relationship between complex behaviors and the activity of many simultaneously recorded neurons. While laten…
A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science
Sebastian Bischoff, Alana Darcher, Michael Deistler +18
Generative models are invaluable in many fields of science because of their ability to capture high-dimensional and complicated distributions, such as photo-realistic images, prote…
Unsupervised Anomaly Detection using Aggregated Normative Diffusion
Alexander Frotscher, Jaivardhan Kapoor, Thomas Wolfers +1
Early detection of anomalies in medical images such as brain MRI is highly relevant for diagnosis and treatment of many conditions. Supervised machine learning methods are limited…
Multiscale Metamorphic VAE for 3D Brain MRI Synthesis
Jaivardhan Kapoor, Jakob H. Macke, Christian F. Baumgartner
Generative modeling of 3D brain MRIs presents difficulties in achieving high visual fidelity while ensuring sufficient coverage of the data distribution. In this work, we propose t…
Variational Inference via Transformations on Distributions
Siddhartha Saxena, Shibhansh Dohare, Jaivardhan Kapoor
Variational inference methods often focus on the problem of efficient model optimization, with little emphasis on the choice of the approximating posterior. In this paper, we revie…