258 citations · 1k across the 30 of their papers we have counts for
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Inference of a mesoscopic population model from population spike trains
Alexandre René, André Longtin, Jakob H. Macke
To understand how rich dynamics emerge in neural populations, we require models exhibiting a wide range of activity patterns while remaining interpretable in terms of connectivity…
Teaching deep neural networks to localize single molecules for super-resolution microscopy
Artur Speiser, Lucas-Raphael Müller, Ulf Matti +5
Single-molecule localization fluorescence microscopy constructs super-resolution images by sequential imaging and computational localization of sparsely activated fluorophores. Acc…
Automatic Posterior Transformation for Likelihood-Free Inference
David S. Greenberg, Marcel Nonnenmacher, Jakob H. Macke
How can one perform Bayesian inference on stochastic simulators with intractable likelihoods? A recent approach is to learn the posterior from adaptively proposed simulations using…
Intrinsic dimension of data representations in deep neural networks
Alessio Ansuini, Alessandro Laio, Jakob H. Macke +1
Deep neural networks progressively transform their inputs across multiple processing layers. What are the geometrical properties of the representations learned by these networks? H…