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- University of TübingenDE25 papers
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5 papers · 1 filter
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process
Christoph Molnar, Timo Freiesleben, Gunnar König +3
Scientists and practitioners increasingly rely on machine learning to model data and draw conclusions. Compared to statistical modeling approaches, machine learning makes fewer exp…
Comment on "Biologically inspired protection of deep networks from adversarial attacks"
Wieland Brendel, Matthias Bethge
A recent paper suggests that Deep Neural Networks can be protected from gradient-based adversarial perturbations by driving the network activations into a highly saturated regime.…
Supervised learning sets benchmark for robust spike detection from calcium imaging signals
Lucas Theis, Philipp Berens, Emmanouil Froudarakis +6
A fundamental challenge in calcium imaging has been to infer the timing of action potentials from the measured noisy calcium fluorescence traces. We systematically evaluate a range…
Mixtures of conditional Gaussian scale mixtures applied to multiscale image representations
Lucas Theis, Reshad Hosseini, Matthias Bethge
We present a probabilistic model for natural images which is based on Gaussian scale mixtures and a simple multiscale representation. In contrast to the dominant approach to modeli…
In All Likelihood, Deep Belief Is Not Enough
Lucas Theis, Sebastian Gerwinn, Fabian Sinz +1
Statistical models of natural stimuli provide an important tool for researchers in the fields of machine learning and computational neuroscience. A canonical way to quantitatively…