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Jakob H. Macke

Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of Munich, Machine Learning in Science, University of Tübingen, Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen

Benchmarking Simulation-Based Inference


Jan 12, 2021
Jan-Matthis Lueckmann, Jan Boelts, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke


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SBI -- A toolkit for simulation-based inference


Jul 22, 2020
Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J. Gonçalves, David S. Greenberg, Jakob H. Macke

* Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann and Conor Durkan contributed equally in shared first authorship. This manuscript has been submitted for consideration to the Journal of Open Source Software (JOSS). 4 pages, no figures; v2: added link to sbi home 

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$\texttt{sbi}$ -- a toolkit for simulation-based inference


Jul 17, 2020
Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J. Gonçalves, David S. Greenberg, Jakob H. Macke

* Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann and Conor Durkan contributed equally in shared first authorship. This manuscript has been submitted for consideration to the Journal of Open Source Software (JOSS). 4 pages, no figures 

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Inference of a mesoscopic population model from population spike trains


Oct 03, 2019
Alexandre René, André Longtin, Jakob H. Macke

* 46 pages, 12 figures 

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Teaching deep neural networks to localize sources in super-resolution microscopy by combining simulation-based learning and unsupervised learning


Jun 27, 2019
Artur Speiser, Srinivas C. Turaga, Jakob H. Macke


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Intrinsic dimension of data representations in deep neural networks


May 29, 2019
Alessio Ansuini, Alessandro Laio, Jakob H. Macke, Davide Zoccolan


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Automatic Posterior Transformation for Likelihood-Free Inference


May 17, 2019
David S. Greenberg, Marcel Nonnenmacher, Jakob H. Macke


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Analyzing biological and artificial neural networks: challenges with opportunities for synergy?


Oct 31, 2018
David G. T. Barrett, Ari S. Morcos, Jakob H. Macke


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Likelihood-free inference with emulator networks


May 23, 2018
Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos, Jakob H. Macke


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Flexible statistical inference for mechanistic models of neural dynamics


Nov 06, 2017
Jan-Matthis Lueckmann, Pedro J. Goncalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, Jakob H. Macke

* NIPS 2017. The first two authors contributed equally 

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Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations


Nov 06, 2017
Marcel Nonnenmacher, Srinivas C. Turaga, Jakob H. Macke


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Fast amortized inference of neural activity from calcium imaging data with variational autoencoders


Nov 06, 2017
Artur Speiser, Jinyao Yan, Evan Archer, Lars Buesing, Srinivas C. Turaga, Jakob H. Macke

* NIPS 2017 

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Hierarchical models for neural population dynamics in the presence of non-stationarity


Oct 12, 2014
Mijung Park, Jakob H. Macke


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