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ORCIDJakob H. Macke
Person information
- affiliation: University of Tübingen, Germany
- affiliation (former): Technical University Munich, Germany
- affiliation (former): Eberhard Karls University of Tübingen, Bernstein Center for Computational Neuroscience
- affiliation (former): Max Planck Institute for Biological Cybernetics
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2020 – today
- 2024
[j13]Matthijs Pals
, Jakob H. Macke, Omri Barak:
Trained recurrent neural networks develop phase-locked limit cycles in a working memory task. PLoS Comput. Biol. 20(2) (2024)- [i28]Julius Vetter, Guy Moss, Cornelius Schröder, Richard Gao, Jakob H. Macke:
Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation. CoRR abs/2402.07808 (2024) - [i27]Jonas Beck, Nathanael Bosch, Michael Deistler, Kyra L. Kadhim, Jakob H. Macke, Philipp Hennig, Philipp Berens:
Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations. CoRR abs/2402.12231 (2024) - [i26]Sebastian Bischoff, Alana Darcher, Michael Deistler, Richard Gao, Franziska Gerken, Manuel Glöckler, Lisa Haxel, Jaivardhan Kapoor, Janne K. Lappalainen, Jakob H. Macke, Guy Moss, Matthijs Pals, Felix Pei, Rachel Rapp, A Erdem Sagtekin, Cornelius Schröder, Auguste Schulz, Zinovia Stefanidi, Shoji Toyota, Linda Ulmer, Julius Vetter:
A Practical Guide to Statistical Distances for Evaluating Generative Models in Science. CoRR abs/2403.12636 (2024) - 2023
- [j12]Jan Boelts, Philipp Harth, Richard Gao, Daniel Udvary, Felipe Yáñez, Daniel Baum, Hans-Christian Hege, Marcel Oberländer, Jakob H. Macke:
Simulation-based inference for efficient identification of generative models in computational connectomics. PLoS Comput. Biol. 19(9) (2023) - [c28]Manuel Glöckler, Michael Deistler, Jakob H. Macke:
Adversarial robustness of amortized Bayesian inference. ICML 2023: 11493-11524 - [c27]Basile Confavreux, Poornima Ramesh, Pedro J. Gonçalves, Jakob H. Macke, Tim P. Vogels:
Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference. NeurIPS 2023 - [c26]Richard Gao, Michael Deistler, Jakob H. Macke:
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation. NeurIPS 2023 - [c25]Jonas Wildberger, Maximilian Dax, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf:
Flow Matching for Scalable Simulation-Based Inference. NeurIPS 2023 - [i25]Jaivardhan Kapoor, Jakob H. Macke, Christian F. Baumgartner:
Multiscale Metamorphic VAE for 3D Brain MRI Synthesis. CoRR abs/2301.03588 (2023) - [i24]Manuel Glöckler, Michael Deistler, Jakob H. Macke:
Adversarial robustness of amortized Bayesian inference. CoRR abs/2305.14984 (2023) - [i23]Cornelius Schröder, Jakob H. Macke:
Simultaneous identification of models and parameters of scientific simulators. CoRR abs/2305.15174 (2023) - [i22]Richard Gao, Michael Deistler, Jakob H. Macke:
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation. CoRR abs/2305.15208 (2023) - [i21]Maximilian Dax, Jonas Wildberger, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf:
Flow Matching for Scalable Simulation-Based Inference. CoRR abs/2305.17161 (2023) - [i20]Mila Gorecki, Jakob H. Macke, Michael Deistler:
Amortized Bayesian Decision Making for simulation-based models. CoRR abs/2312.02674 (2023) - [i19]Guy Moss, Vjeran Visnjevic, Olaf Eisen, Falk M. Oraschewski, Cornelius Schröder, Jakob H. Macke, Reinhard Drews:
Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layers. CoRR abs/2312.02997 (2023) - 2022
- [c24]Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, Jakob H. Macke:
Group equivariant neural posterior estimation. ICLR 2022 - [c23]Manuel Glöckler, Michael Deistler, Jakob H. Macke:
Variational methods for simulation-based inference. ICLR 2022 - [c22]Poornima Ramesh, Jan-Matthis Lueckmann, Jan Boelts, Álvaro Tejero-Cantero, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke:
GATSBI: Generative Adversarial Training for Simulation-Based Inference. ICLR 2022 - [c21]Jonas Beck, Michael Deistler, Yves Bernaerts, Jakob H. Macke, Philipp Berens:
Efficient identification of informative features in simulation-based inference. NeurIPS 2022 - [c20]Michael Deistler, Pedro J. Gonçalves, Jakob H. Macke:
Truncated proposals for scalable and hassle-free simulation-based inference. NeurIPS 2022 - [i18]Manuel Glöckler, Michael Deistler, Jakob H. Macke:
Variational methods for simulation-based inference. CoRR abs/2203.04176 (2022) - [i17]Poornima Ramesh, Jan-Matthis Lueckmann, Jan Boelts, Álvaro Tejero-Cantero, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke:
GATSBI: Generative Adversarial Training for Simulation-Based Inference. CoRR abs/2203.06481 (2022) - [i16]Michael Deistler, Pedro J. Gonçalves, Jakob H. Macke:
Truncated proposals for scalable and hassle-free simulation-based inference. CoRR abs/2210.04815 (2022) - [i15]Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf:
Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference. CoRR abs/2210.05686 (2022) - [i14]Jonas Beck, Michael Deistler, Yves Bernaerts, Jakob H. Macke, Philipp Berens:
Efficient identification of informative features in simulation-based inference. CoRR abs/2210.11915 (2022) - [i13]Jonas Wildberger, Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf:
Adapting to noise distribution shifts in flow-based gravitational-wave inference. CoRR abs/2211.08801 (2022) - 2021
- [c19]Jan-Matthis Lueckmann, Jan Boelts, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke:
Benchmarking Simulation-Based Inference. AISTATS 2021: 343-351 - [i12]Jan-Matthis Lueckmann, Jan Boelts, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke:
Benchmarking Simulation-Based Inference. CoRR abs/2101.04653 (2021) - [i11]Maximilian Dax, Stephen R. Green, Jonathan Gair, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf:
Real-time gravitational-wave science with neural posterior estimation. CoRR abs/2106.12594 (2021) - [i10]Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, Jakob H. Macke:
Group equivariant neural posterior estimation. CoRR abs/2111.13139 (2021) - [i9]Alexander Lavin, Hector Zenil, Brooks Paige, David Krakauer, Justin Gottschlich, Tim Mattson, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atilim Günes Baydin, Carina Prunkl, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob H. Macke, Kyle Cranmer, Jiaxin Zhang, Haruko M. Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer:
Simulation Intelligence: Towards a New Generation of Scientific Methods. CoRR abs/2112.03235 (2021) - 2020
- [j11]Álvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J. Gonçalves, David S. Greenberg, Jakob H. Macke:
sbi: A toolkit for simulation-based inference. J. Open Source Softw. 5(52): 2505 (2020) - [j10]Alexandre René, André Longtin, Jakob H. Macke:
Inference of a Mesoscopic Population Model from Population Spike Trains. Neural Comput. 32(8): 1448-1498 (2020) - [i8]Álvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J. Gonçalves, David S. Greenberg, Jakob H. Macke:
SBI - A toolkit for simulation-based inference. CoRR abs/2007.09114 (2020)
2010 – 2019
- 2019
- [c18]David S. Greenberg, Marcel Nonnenmacher, Jakob H. Macke:
Automatic Posterior Transformation for Likelihood-Free Inference. ICML 2019: 2404-2414 - [c17]Alessio Ansuini, Alessandro Laio, Jakob H. Macke, Davide Zoccolan:
Intrinsic dimension of data representations in deep neural networks. NeurIPS 2019: 6109-6119 - [i7]David S. Greenberg, Marcel Nonnenmacher, Jakob H. Macke:
Automatic Posterior Transformation for Likelihood-Free Inference. CoRR abs/1905.07488 (2019) - [i6]Alessio Ansuini, Alessandro Laio, Jakob H. Macke, Davide Zoccolan:
Intrinsic dimension of data representations in deep neural networks. CoRR abs/1905.12784 (2019) - [i5]Artur Speiser, Srinivas C. Turaga, Jakob H. Macke:
Teaching deep neural networks to localize sources in super-resolution microscopy by combining simulation-based learning and unsupervised learning. CoRR abs/1907.00770 (2019) - [i4]Alexandre René, André Longtin, Jakob H. Macke:
Inference of a mesoscopic population model from population spike trains. CoRR abs/1910.01618 (2019) - 2018
- [j9]Philipp Berens, Jeremy Freeman, Thomas Deneux, Nicolay Chenkov, Thomas McColgan, Artur Speiser, Jakob H. Macke, Srinivas C. Turaga, Patrick J. Mineault, Peter Rupprecht, Stephan Gerhard, Rainer W. Friedrich, Johannes Friedrich, Liam Paninski, Marius Pachitariu, Kenneth D. Harris, Ben Bolte, Timothy A. Machado, Dario Ringach, Jasmine Stone, Luke E. Rogerson, Nicolas J. Sofroniew, Jacob Reimer, Emmanouil Froudarakis, Thomas Euler, Miroslav Román Rosón, Lucas Theis, Andreas S. Tolias, Matthias Bethge:
Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. PLoS Comput. Biol. 14(5) (2018) - [c16]Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos, Jakob H. Macke:
Likelihood-free inference with emulator networks. AABI 2018: 32-53 - [i3]Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos, Jakob H. Macke:
Likelihood-free inference with emulator networks. CoRR abs/1805.09294 (2018) - [i2]David G. T. Barrett, Ari S. Morcos, Jakob H. Macke:
Analyzing biological and artificial neural networks: challenges with opportunities for synergy? CoRR abs/1810.13373 (2018) - 2017
- [j8]Marcel Nonnenmacher, Christian Behrens, Philipp Berens, Matthias Bethge, Jakob H. Macke:
Signatures of criticality arise from random subsampling in simple population models. PLoS Comput. Biol. 13(10) (2017) - [c15]Jan-Matthis Lueckmann, Pedro J. Gonçalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, Jakob H. Macke:
Flexible statistical inference for mechanistic models of neural dynamics. NIPS 2017: 1289-1299 - [c14]Artur Speiser, Jinyao Yan, Evan W. Archer, Lars Buesing, Srinivas C. Turaga, Jakob H. Macke:
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders. NIPS 2017: 4024-4034 - [c13]Marcel Nonnenmacher, Srinivas C. Turaga, Jakob H. Macke:
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations. NIPS 2017: 5702-5712 - [i1]Artur Speiser, Jinyao Yan, Evan Archer, Lars Buesing, Srinivas C. Turaga, Jakob H. Macke:
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders. CoRR abs/1711.01846 (2017) - 2015
- [c12]Mijung Park, Gergo Bohner, Jakob H. Macke:
Unlocking neural population non-stationarities using hierarchical dynamics models. NIPS 2015: 145-153 - 2014
- [c11]Evan W. Archer, Urs Köster, Jonathan W. Pillow, Jakob H. Macke:
Low-dimensional models of neural population activity in sensory cortical circuits. NIPS 2014: 343-351 - [c10]Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke:
A Bayesian model for identifying hierarchically organised states in neural population activity. NIPS 2014: 3095-3103 - [r1]Jakob H. Macke:
Electrophysiology Analysis, Bayesian. Encyclopedia of Computational Neuroscience 2014 - 2013
- [j7]Jakob H. Macke, Iain Murray, Peter E. Latham:
Estimation Bias in Maximum Entropy Models. Entropy 15(8): 3109-3119 (2013) - [c9]Srinivas C. Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Häusser, Jakob H. Macke:
Inferring neural population dynamics from multiple partial recordings of the same neural circuit. NIPS 2013: 539-547 - 2012
- [c8]Lars Buesing, Jakob H. Macke, Maneesh Sahani:
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS 2012: 1691-1699 - 2011
- [j6]Jakob H. Macke, Philipp Berens, Matthias Bethge:
Statistical Analysis of Multi-Cell Recordings: Linking Population Coding Models to Experimental Data. Frontiers Comput. Neurosci. 5: 35 (2011) - [j5]Jakob H. Macke, Sebastian Gerwinn, Leonard E. White, Matthias Kaschube, Matthias Bethge:
Gaussian process methods for estimating cortical maps. NeuroImage 56(2): 570-581 (2011) - [c7]Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani:
Empirical models of spiking in neural populations. NIPS 2011: 1350-1358 - [c6]Jakob H. Macke, Iain Murray, Peter E. Latham:
How biased are maximum entropy models? NIPS 2011: 2034-2042 - 2010
- [j4]Sebastian Gerwinn, Jakob H. Macke, Matthias Bethge:
Bayesian inference for generalized linear models for spiking neurons. Frontiers Comput. Neurosci. 4: 12 (2010) - [j3]Dmitry R. Lyamzin, Jakob H. Macke, Nicholas A. Lesica:
Modeling Population Spike Trains with Specified Time-Varying Spike Rates, Trial-to-Trial Variability, and Pairwise Signal and Noise Correlations. Frontiers Comput. Neurosci. 4: 144 (2010)
2000 – 2009
- 2009
- [j2]Sebastian Gerwinn, Jakob H. Macke, Matthias Bethge:
Bayesian population decoding of spiking neurons. Frontiers Comput. Neurosci. 3: 21 (2009) - [j1]Jakob H. Macke, Philipp Berens, Alexander S. Ecker, Andreas S. Tolias, Matthias Bethge:
Generating Spike Trains with Specified Correlation Coefficients. Neural Comput. 21(2): 397-423 (2009) - [c5]Jakob H. Macke, Sebastian Gerwinn, Leonard E. White, Matthias Kaschube, Matthias Bethge:
Bayesian estimation of orientation preference maps. NIPS 2009: 1195-1203 - 2007
- [c4]Matthias Bethge, Sebastian Gerwinn, Jakob H. Macke:
Unsupervised learning of a steerable basis for invariant image representations. Human Vision and Electronic Imaging 2007: 64920C - [c3]Sebastian Gerwinn, Jakob H. Macke, Matthias W. Seeger, Matthias Bethge:
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior. NIPS 2007: 529-536 - [c2]Jakob H. Macke, Guenther Zeck, Matthias Bethge:
Receptive Fields without Spike-Triggering. NIPS 2007: 969-976 - 2006
- [c1]Julian Laub, Jakob H. Macke, Klaus-Robert Müller, Felix A. Wichmann:
Inducing Metric Violations in Human Similarity Judgements. NIPS 2006: 777-784
Coauthor Index
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