128 citations · 293 across the 18 of their papers we have counts for
19 papers
A Probabilistic Approach to Self-Supervised Learning using Cyclical Stochastic Gradient MCMC
Masoumeh Javanbakhat, Christoph Lippert
In this paper we present a practical Bayesian self-supervised learning method with Cyclical Stochastic Gradient Hamiltonian Monte Carlo (cSGHMC). Within this framework, we place a…
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…
DCID: Deep Canonical Information Decomposition
Alexander Rakowski, Christoph Lippert
We consider the problem of identifying the signal shared between two one-dimensional target variables, in the presence of additional multivariate observations. Canonical Correlatio…
Iterative Patch Selection for High-Resolution Image Recognition
Benjamin Bergner, Christoph Lippert, Aravindh Mahendran
High-resolution images are prevalent in various applications, such as autonomous driving and computer-aided diagnosis. However, training neural networks on such images is computati…
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…