6 papers
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
Manuel Haussmann, Sebastian Gerwinn, Andreas Look +2
Neural Stochastic Differential Equations model a dynamical environment with neural nets assigned to their drift and diffusion terms. The high expressive power of their nonlinearity…
Deep Active Learning with Adaptive Acquisition
Manuel Haussmann, Fred A. Hamprecht, Melih Kandemir
Model selection is treated as a standard performance boosting step in many machine learning applications. Once all other properties of a learning problem are fixed, the model is se…
Bayesian Evidential Deep Learning with PAC Regularization
Manuel Haussmann, Sebastian Gerwinn, Melih Kandemir
We propose a novel method for closed-form predictive distribution modeling with neural nets. In quantifying prediction uncertainty, we build on Evidential Deep Learning, which has…
Deep-Learning Jets with Uncertainties and More
Sven Bollweg, Manuel Haussmann, Gregor Kasieczka +3
Bayesian neural networks allow us to keep track of uncertainties, for example in top tagging, by learning a tagger output together with an error band. We illustrate the main featur…
LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos
Elke Kirschbaum, Manuel Haußmann, Steffen Wolf +6
Neuronal assemblies, loosely defined as subsets of neurons with reoccurring spatio-temporally coordinated activation patterns, or "motifs", are thought to be building blocks of neu…
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation
Manuel Haussmann, Fred A. Hamprecht, Melih Kandemir
We propose a new Bayesian Neural Net formulation that affords variational inference for which the evidence lower bound is analytically tractable subject to a tight approximation. W…