128 citations · 293 across the 18 of their papers we have counts for
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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…
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…
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…
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout
Yamen Ali, Aiham Taleb, Marina M. -C. Höhne +1
Self-supervised learning methods can be used to learn meaningful representations from unlabeled data that can be transferred to supervised downstream tasks to reduce the need for l…
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…