Jakob H. Macke

According to our database, Jakob H. Macke authored at least 61 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Trained recurrent neural networks develop phase-locked limit cycles in a working memory task.

Matthijs Pals, Jakob H. Macke, Omri Barak

PLoS Comput. Biol., February, 2024

Amortized Bayesian Decision Making for simulation-based models.

Mila Gorecki, Jakob H. Macke, Michael Deistler

Trans. Mach. Learn. Res., 2024

Generating realistic neurophysiological time series with denoising diffusion probabilistic models.

Julius Vetter, Jakob H. Macke, Richard Gao

Patterns, 2024

Neural timescales from a computational perspective.

Roxana Zeraati, Anna Levina, Jakob H. Macke, Richard Gao

CoRR, 2024

Real-time gravitational-wave inference for binary neutron stars using machine learning.

Maximilian Dax, Stephen R. Green, Jonathan Gair, Nihar Gupte, Michael Pürrer, Vivien Raymond, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf

CoRR, 2024

Latent Diffusion for Neural Spiking Data.

Jaivardhan Kapoor, Auguste Schulz, Julius Vetter, Felix Pei, Richard Gao, Jakob H. Macke

CoRR, 2024

Inferring stochastic low-rank recurrent neural networks from neural data.

Matthijs Pals, A Erdem Sagtekin, Felix Pei, Manuel Glöckler, Jakob H. Macke

CoRR, 2024

A Practical Guide to Statistical Distances for Evaluating Generative Models in Science.

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

CoRR, 2024

Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation.

Julius Vetter, Guy Moss, Cornelius Schröder, Richard Gao, Jakob H. Macke

CoRR, 2024

Simultaneous identification of models and parameters of scientific simulators.

Cornelius Schröder, Jakob H. Macke

Proceedings of the Forty-first International Conference on Machine Learning, 2024

All-in-one simulation-based inference.

Manuel Glöckler, Michael Deistler, Christian Dietrich Weilbach, Frank Wood, Jakob H. Macke

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations.

Jonas Beck, Nathanael Bosch, Michael Deistler, Kyra L. Kadhim, Jakob H. Macke, Philipp Hennig, Philipp Berens

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Simulation-based inference for efficient identification of generative models in computational connectomics.

Jan Boelts, Philipp Harth, Richard Gao, Daniel Udvary, Felipe Yáñez, Daniel Baum, Hans-Christian Hege, Marcel Oberländer, Jakob H. Macke

PLoS Comput. Biol., 2023

Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layers.

Guy Moss, Vjeran Visnjevic, Olaf Eisen, Falk M. Oraschewski, Cornelius Schröder, Jakob H. Macke, Reinhard Drews

CoRR, 2023

Multiscale Metamorphic VAE for 3D Brain MRI Synthesis.

Jaivardhan Kapoor, Jakob H. Macke, Christian F. Baumgartner

CoRR, 2023

Flow Matching for Scalable Simulation-Based Inference.

Jonas Wildberger, Maximilian Dax, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation.

Richard Gao, Michael Deistler, Jakob H. Macke

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference.

Basile Confavreux, Poornima Ramesh, Pedro J. Gonçalves, Jakob H. Macke, Tim P. Vogels

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Adversarial robustness of amortized Bayesian inference.

Manuel Glöckler, Michael Deistler, Jakob H. Macke

Proceedings of the International Conference on Machine Learning, 2023

2022

Adapting to noise distribution shifts in flow-based gravitational-wave inference.

Jonas Wildberger, Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf

CoRR, 2022

Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference.

Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf

CoRR, 2022

Truncated proposals for scalable and hassle-free simulation-based inference.

Michael Deistler, Pedro J. Gonçalves, Jakob H. Macke

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Efficient identification of informative features in simulation-based inference.

Jonas Beck, Michael Deistler, Yves Bernaerts, Jakob H. Macke, Philipp Berens

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

GATSBI: Generative Adversarial Training for Simulation-Based Inference.

Poornima Ramesh, Jan-Matthis Lueckmann, Jan Boelts, Álvaro Tejero-Cantero, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke

Proceedings of the Tenth International Conference on Learning Representations, 2022

Variational methods for simulation-based inference.

Manuel Glöckler, Michael Deistler, Jakob H. Macke

Proceedings of the Tenth International Conference on Learning Representations, 2022

Group equivariant neural posterior estimation.

Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, Jakob H. Macke

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Simulation Intelligence: Towards a New Generation of Scientific Methods.

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

CoRR, 2021

Real-time gravitational-wave science with neural posterior estimation.

Maximilian Dax, Stephen R. Green, Jonathan Gair, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf

CoRR, 2021

Benchmarking Simulation-Based Inference.

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

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Inference of a Mesoscopic Population Model from Population Spike Trains.

Alexandre René, André Longtin, Jakob H. Macke

Neural Comput., 2020

sbi: A toolkit for simulation-based inference.

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

J. Open Source Softw., 2020

2019

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

Artur Speiser, Srinivas C. Turaga, Jakob H. Macke

CoRR, 2019

Intrinsic dimension of data representations in deep neural networks.

Alessio Ansuini, Alessandro Laio, Jakob H. Macke, Davide Zoccolan

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Automatic Posterior Transformation for Likelihood-Free Inference.

David S. Greenberg, Marcel Nonnenmacher, Jakob H. Macke

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data.

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

PLoS Comput. Biol., 2018

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

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

CoRR, 2018

Likelihood-free inference with emulator networks.

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

Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2018

2017

Signatures of criticality arise from random subsampling in simple population models.

Marcel Nonnenmacher, Christian Behrens, Philipp Berens, Matthias Bethge, Jakob H. Macke

PLoS Comput. Biol., 2017

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

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

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

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

Marcel Nonnenmacher, Srinivas C. Turaga, Jakob H. Macke

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Flexible statistical inference for mechanistic models of neural dynamics.

Jan-Matthis Lueckmann, Pedro J. Gonçalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, Jakob H. Macke

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2015

Unlocking neural population non-stationarities using hierarchical dynamics models.

Mijung Park, Gergo Bohner, Jakob H. Macke

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Electrophysiology Analysis, Bayesian.

Jakob H. Macke

Proceedings of the Encyclopedia of Computational Neuroscience, 2014

A Bayesian model for identifying hierarchically organised states in neural population activity.

Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Low-dimensional models of neural population activity in sensory cortical circuits.

Evan W. Archer, Urs Köster, Jonathan W. Pillow, Jakob H. Macke

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

Estimation Bias in Maximum Entropy Models.

Jakob H. Macke, Iain Murray, Peter E. Latham

Entropy, 2013

Inferring neural population dynamics from multiple partial recordings of the same neural circuit.

Srinivas C. Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Häusser, Jakob H. Macke

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

Spectral learning of linear dynamics from generalised-linear observations with application to neural population data.

Lars Buesing, Jakob H. Macke, Maneesh Sahani

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

Gaussian process methods for estimating cortical maps.

Jakob H. Macke, Sebastian Gerwinn, Leonard E. White, Matthias Kaschube, Matthias Bethge

NeuroImage, 2011

Statistical Analysis of Multi-Cell Recordings: Linking Population Coding Models to Experimental Data.

Jakob H. Macke, Philipp Berens, Matthias Bethge

Frontiers Comput. Neurosci., 2011

How biased are maximum entropy models?

Jakob H. Macke, Iain Murray, Peter E. Latham

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Empirical models of spiking in neural populations.

Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Modeling Population Spike Trains with Specified Time-Varying Spike Rates, Trial-to-Trial Variability, and Pairwise Signal and Noise Correlations.

Dmitry R. Lyamzin, Jakob H. Macke, Nicholas A. Lesica

Frontiers Comput. Neurosci., 2010

Bayesian inference for generalized linear models for spiking neurons.

Sebastian Gerwinn, Jakob H. Macke, Matthias Bethge

Frontiers Comput. Neurosci., 2010

2009

Generating Spike Trains with Specified Correlation Coefficients.

Jakob H. Macke, Philipp Berens, Alexander S. Ecker, Andreas S. Tolias, Matthias Bethge

Neural Comput., 2009

Bayesian population decoding of spiking neurons.

Sebastian Gerwinn, Jakob H. Macke, Matthias Bethge

Frontiers Comput. Neurosci., 2009

Bayesian estimation of orientation preference maps.

Jakob H. Macke, Sebastian Gerwinn, Leonard E. White, Matthias Kaschube, Matthias Bethge

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2007

Receptive Fields without Spike-Triggering.

Jakob H. Macke, Guenther Zeck, Matthias Bethge

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Bayesian Inference for Spiking Neuron Models with a Sparsity Prior.

Sebastian Gerwinn, Jakob H. Macke, Matthias W. Seeger, Matthias Bethge

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Unsupervised learning of a steerable basis for invariant image representations.

Matthias Bethge, Sebastian Gerwinn, Jakob H. Macke

Proceedings of the Human Vision and Electronic Imaging XII, San Jose, CA, USA, January 29, 2007

2006

Inducing Metric Violations in Human Similarity Judgements.

Julian Laub, Jakob H. Macke, Klaus-Robert Müller, Felix A. Wichmann

Proceedings of the Advances in Neural Information Processing Systems 19, 2006