Jakob H. Macke

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 54

Top Topics

Data Representations

Bayesian Inference

Top Venues

PLoS Comput. Biol.

Publications

Mila Gorecki, Jakob H. Macke, Michael Deistler
Amortized Bayesian Decision Making for simulation-based models. Trans. Mach. Learn. Res. 2024 (2024)
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 (2024)
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 (2024)
Maximilian Dax, Stephen R. Green, Jonathan Gair, Nihar Gupte, Michael Pürrer, Vivien Raymond, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf
Real-time gravitational-wave inference for binary neutron stars using machine learning. CoRR (2024)
Julius Vetter, Guy Moss, Cornelius Schröder, Richard Gao, Jakob H. Macke
Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation. CoRR (2024)
Jaivardhan Kapoor, Auguste Schulz, Julius Vetter, Felix Pei, Richard Gao, Jakob H. Macke
Latent Diffusion for Neural Spiking Data. CoRR (2024)
Manuel Glöckler, Michael Deistler, Christian Weilbach, Frank Wood, Jakob H. Macke
All-in-one simulation-based inference. CoRR (2024)
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)
Matthijs Pals, A Erdem Sagtekin, Felix Pei, Manuel Glöckler, Jakob H. Macke
Inferring stochastic low-rank recurrent neural networks from neural data. CoRR (2024)
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)
Jaivardhan Kapoor, Jakob H. Macke, Christian F. Baumgartner
Multiscale Metamorphic VAE for 3D Brain MRI Synthesis. CoRR (2023)
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 (2023)
Richard Gao, Michael Deistler, Jakob H. Macke
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation. NeurIPS (2023)
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)
Richard Gao, Michael Deistler, Jakob H. Macke
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation. CoRR (2023)
Mila Gorecki, Jakob H. Macke, Michael Deistler
Amortized Bayesian Decision Making for simulation-based models. CoRR (2023)
Jonas Wildberger, Maximilian Dax, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf
Flow Matching for Scalable Simulation-Based Inference. NeurIPS (2023)
Maximilian Dax, Jonas Wildberger, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf
Flow Matching for Scalable Simulation-Based Inference. CoRR (2023)
Manuel Glöckler, Michael Deistler, Jakob H. Macke
Adversarial robustness of amortized Bayesian inference. CoRR (2023)
Manuel Glöckler, Michael Deistler, Jakob H. Macke
Adversarial robustness of amortized Bayesian inference. ICML (2023)
Cornelius Schröder, Jakob H. Macke
Simultaneous identification of models and parameters of scientific simulators. CoRR (2023)
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 (2022)
Manuel Glöckler, Michael Deistler, Jakob H. Macke
Variational methods for simulation-based inference. CoRR (2022)
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 (2022)
Jonas Beck, Michael Deistler, Yves Bernaerts, Jakob H. Macke, Philipp Berens
Efficient identification of informative features in simulation-based inference. NeurIPS (2022)
Jonas Beck, Michael Deistler, Yves Bernaerts, Jakob H. Macke, Philipp Berens
Efficient identification of informative features in simulation-based inference. CoRR (2022)
Michael Deistler, Pedro J. Gonçalves, Jakob H. Macke
Truncated proposals for scalable and hassle-free simulation-based inference. CoRR (2022)
Michael Deistler, Pedro J. Gonçalves, Jakob H. Macke
Truncated proposals for scalable and hassle-free simulation-based inference. NeurIPS (2022)
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 (2022)
Manuel Glöckler, Michael Deistler, Jakob H. Macke
Variational methods for simulation-based inference. ICLR (2022)
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)
Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, Jakob H. Macke
Group equivariant neural posterior estimation. ICLR (2022)
Jan-Matthis Lueckmann, Jan Boelts, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke
Benchmarking Simulation-Based Inference. CoRR (2021)
Jan-Matthis Lueckmann, Jan Boelts, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke
Benchmarking Simulation-Based Inference. AISTATS (2021)
Maximilian Dax, Stephen R. Green, Jonathan Gair, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf
Real-time gravitational-wave science with neural posterior estimation. CoRR (2021)
Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, Jakob H. Macke
Group equivariant neural posterior estimation. CoRR (2021)
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) (2020)
Alexandre René, André Longtin, Jakob H. Macke
Inference of a Mesoscopic Population Model from Population Spike Trains. Neural Comput. 32 (8) (2020)
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 (2020)
David S. Greenberg, Marcel Nonnenmacher, Jakob H. Macke
Automatic Posterior Transformation for Likelihood-Free Inference. CoRR (2019)
David S. Greenberg, Marcel Nonnenmacher, Jakob H. Macke
Automatic Posterior Transformation for Likelihood-Free Inference. ICML (2019)
Alessio Ansuini, Alessandro Laio, Jakob H. Macke, Davide Zoccolan
Intrinsic dimension of data representations in deep neural networks. CoRR (2019)
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 (2019)
Alexandre René, André Longtin, Jakob H. Macke
Inference of a mesoscopic population model from population spike trains. CoRR (2019)
Alessio Ansuini, Alessandro Laio, Jakob H. Macke, Davide Zoccolan
Intrinsic dimension of data representations in deep neural networks. NeurIPS (2019)
David G. T. Barrett, Ari S. Morcos, Jakob H. Macke
Analyzing biological and artificial neural networks: challenges with opportunities for synergy? CoRR (2018)
Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos, Jakob H. Macke
Likelihood-free inference with emulator networks. CoRR (2018)
Philipp Berens, Jeremy Freeman, Thomas Deneux, Nicolay Chenkov, Thomas McColgan, Artur Speiser, Jakob H. Macke, Srinivas C. Turaga, Patrick 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)
Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos, Jakob H. Macke
Likelihood-free inference with emulator networks. AABI (2018)
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)
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)
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)
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)
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 (2017)
Mijung Park, Gergo Bohner, Jakob H. Macke
Unlocking neural population non-stationarities using hierarchical dynamics models. NIPS (2015)
Evan W. Archer, Urs Köster, Jonathan W. Pillow, Jakob H. Macke
Low-dimensional models of neural population activity in sensory cortical circuits. NIPS (2014)
Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke
A Bayesian model for identifying hierarchically organised states in neural population activity. NIPS (2014)
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)
Jakob H. Macke, Iain Murray, Peter E. Latham
Estimation Bias in Maximum Entropy Models. Entropy 15 (8) (2013)
Lars Buesing, Jakob H. Macke, Maneesh Sahani
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS (2012)
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)
Jakob H. Macke, Sebastian Gerwinn, Leonard E. White, Matthias Kaschube, Matthias Bethge
Gaussian process methods for estimating cortical maps. NeuroImage 56 (2) (2011)
Jakob H. Macke, Iain Murray, Peter E. Latham
How biased are maximum entropy models? NIPS (2011)
Jakob H. Macke, Philipp Berens, Matthias Bethge
Statistical Analysis of Multi-Cell Recordings: Linking Population Coding Models to Experimental Data. Frontiers Comput. Neurosci. 5 (2011)
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 (2010)
Sebastian Gerwinn, Jakob H. Macke, Matthias Bethge
Bayesian inference for generalized linear models for spiking neurons. Frontiers Comput. Neurosci. 4 (2010)
Jakob H. Macke, Philipp Berens, Alexander S. Ecker, Andreas S. Tolias, Matthias Bethge
Generating Spike Trains with Specified Correlation Coefficients. Neural Comput. 21 (2) (2009)
Sebastian Gerwinn, Jakob H. Macke, Matthias Bethge
Bayesian population decoding of spiking neurons. Frontiers Comput. Neurosci. 3 (2009)
Jakob H. Macke, Sebastian Gerwinn, Leonard E. White, Matthias Kaschube, Matthias Bethge
Bayesian estimation of orientation preference maps. NIPS (2009)
Matthias Bethge, Sebastian Gerwinn, Jakob H. Macke
Unsupervised learning of a steerable basis for invariant image representations. Human Vision and Electronic Imaging (2007)
Sebastian Gerwinn, Jakob H. Macke, Matthias W. Seeger, Matthias Bethge
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior. NIPS (2007)
Jakob H. Macke, Guenther Zeck, Matthias Bethge
Receptive Fields without Spike-Triggering. NIPS (2007)
Julian Laub, Jakob H. Macke, Klaus-Robert Müller, Felix A. Wichmann
Inducing Metric Violations in Human Similarity Judgements. NIPS (2006)