81 citations · 175 across the 16 of their papers we have counts for
3 papers · 1 filter
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…