149 citations · 338 across the 11 of their papers we have counts for
6 papers · 1 filter
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
David Klindt, Lukas Schott, Yash Sharma +4
We construct an unsupervised learning model that achieves nonlinear disentanglement of underlying factors of variation in naturalistic videos. Previous work suggests that represent…
Towards causal generative scene models via competition of experts
Julius von Kügelgen, Ivan Ustyuzhaninov, Peter Gehler +2
Learning how to model complex scenes in a modular way with recombinable components is a pre-requisite for higher-order reasoning and acting in the physical world. However, current…
Accurate, reliable and fast robustness evaluation
Wieland Brendel, Jonas Rauber, Matthias Kümmerer +2
Throughout the past five years, the susceptibility of neural networks to minimal adversarial perturbations has moved from a peculiar phenomenon to a core issue in Deep Learning. De…
Trace your sources in large-scale data: one ring to find them all
Alexander Böttcher, Wieland Brendel, Bernhard Englitz +1
An important preprocessing step in most data analysis pipelines aims to extract a small set of sources that explain most of the data. Currently used algorithms for blind source sep…
Neural system identification for large populations separating "what" and "where"
David A. Klindt, Alexander S. Ecker, Thomas Euler +1
Neuroscientists classify neurons into different types that perform similar computations at different locations in the visual field. Traditional methods for neural system identifica…
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…