Inducing Metric Violations in Human Similarity Judgements. - Julian Laub; Jakob H. Macke; Klaus-Robert Müller; Felix A. Wichmann (NIPS, 2006)

Unsupervised learning of a steerable basis for invariant image representations. - Matthias Bethge; Sebastian Gerwinn; Jakob H. Macke (Human Vision and Electronic Imaging, 2007)

Bayesian Inference for Spiking Neuron Models with a Sparsity Prior. - Sebastian Gerwinn; Jakob H. Macke; Matthias W. Seeger; Matthias Bethge (NIPS, 2007)

Receptive Fields without Spike-Triggering. - Jakob H. Macke; Guenther Zeck; Matthias Bethge (NIPS, 2007)

Bayesian estimation of orientation preference maps. - Jakob H. Macke; Sebastian Gerwinn; Leonard E. White; Matthias Kaschube; Matthias Bethge (NIPS, 2009)

Bayesian population decoding of spiking neurons. - Sebastian Gerwinn; Jakob H. Macke; Matthias Bethge (Frontiers Comput. Neurosci., 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 inference for generalized linear models for spiking neurons. - Sebastian Gerwinn; Jakob H. Macke; Matthias Bethge (Frontiers Comput. Neurosci., 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)

Empirical models of spiking in neural populations. - Jakob H. Macke; Lars Buesing; John P. Cunningham; Byron M. Yu; Krishna V. Shenoy; Maneesh Sahani (NIPS, 2011)

How biased are maximum entropy models? - Jakob H. Macke; Iain Murray 001; Peter E. Latham (NIPS, 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)

Gaussian process methods for estimating cortical maps. - Jakob H. Macke; Sebastian Gerwinn; Leonard E. White; Matthias Kaschube; Matthias Bethge (NeuroImage, 2011)

Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. - Lars Buesing; Jakob H. Macke; Maneesh Sahani (NIPS, 2012)

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 (NIPS, 2013)

Estimation Bias in Maximum Entropy Models. - Jakob H. Macke; Iain Murray 001; Peter E. Latham (Entropy, 2013)

Low-dimensional models of neural population activity in sensory cortical circuits. - Evan W. Archer; Urs Köster; Jonathan W. Pillow; Jakob H. Macke (NIPS, 2014)

A Bayesian model for identifying hierarchically organised states in neural population activity. - Patrick Putzky; Florian Franzen; Giacomo Bassetto; Jakob H. Macke (NIPS, 2014)

Electrophysiology Analysis, Bayesian. - Jakob H. Macke (Encyclopedia of Computational Neuroscience, 2014)

Unlocking neural population non-stationarities using hierarchical dynamics models. - Mijung Park; Gergo Bohner; Jakob H. Macke (NIPS, 2015)

Flexible statistical inference for mechanistic models of neural dynamics. - Jan-Matthis Lueckmann; Pedro J. Gonçalves; Giacomo Bassetto; Kaan Öcal; Marcel Nonnenmacher; Jakob H. Macke (NIPS, 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 (NIPS, 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 (NIPS, 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)

Likelihood-free inference with emulator networks. - Jan-Matthis Lueckmann; Giacomo Bassetto; Theofanis Karaletsos; Jakob H. Macke (AABI, 2018)

Analyzing biological and artificial neural networks: challenges with opportunities for synergy? - David G. T. Barrett; Ari S. Morcos; Jakob H. Macke (CoRR, 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; et al. (PLoS Comput. Biol., 2018)

Automatic Posterior Transformation for Likelihood-Free Inference. - David S. Greenberg; Marcel Nonnenmacher; Jakob H. Macke (ICML, 2019)

Intrinsic dimension of data representations in deep neural networks. - Alessio Ansuini; Alessandro Laio; Jakob H. Macke; Davide Zoccolan (NeurIPS, 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)

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)

Inference of a Mesoscopic Population Model from Population Spike Trains. - Alexandre René; André Longtin; Jakob H. Macke (Neural Comput., 2020)

Benchmarking Simulation-Based Inference. - Jan-Matthis Lueckmann; Jan Boelts; David S. Greenberg; Pedro J. Gonçalves; Jakob H. Macke (AISTATS, 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)

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; et al. (CoRR, 2021)

Group equivariant neural posterior estimation. - Maximilian Dax; Stephen R. Green; Jonathan Gair; Michael Deistler; Bernhard Schölkopf; Jakob H. Macke (ICLR, 2022)

Variational methods for simulation-based inference. - Manuel Glöckler; Michael Deistler; Jakob H. Macke (ICLR, 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 (ICLR, 2022)

Efficient identification of informative features in simulation-based inference. - Jonas Beck; Michael Deistler; Yves Bernaerts; Jakob H. Macke; Philipp Berens (NeurIPS, 2022)

Truncated proposals for scalable and hassle-free simulation-based inference. - Michael Deistler; Pedro J. Gonçalves; Jakob H. Macke (NeurIPS, 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)

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)

Adversarial robustness of amortized Bayesian inference. - Manuel Glöckler; Michael Deistler; Jakob H. Macke (ICML, 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 (NeurIPS, 2023)

Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation. - Richard Gao; Michael Deistler; Jakob H. Macke (NeurIPS, 2023)

Flow Matching for Scalable Simulation-Based Inference. - Jonas Wildberger; Maximilian Dax; Simon Buchholz; Stephen R. Green; Jakob H. Macke; Bernhard Schölkopf (NeurIPS, 2023)

Multiscale Metamorphic VAE for 3D Brain MRI Synthesis. - Jaivardhan Kapoor; Jakob H. Macke; Christian F. Baumgartner (CoRR, 2023)

Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layers. - Guy Moss 001; Vjeran Visnjevic; Olaf Eisen; Falk M. Oraschewski; Cornelius Schröder; Jakob H. Macke; Reinhard Drews (CoRR, 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)

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 (ICML, 2024)

All-in-one simulation-based inference. - Manuel Glöckler; Michael Deistler; Christian Dietrich Weilbach; Frank Wood; Jakob H. Macke (ICML, 2024)

Simultaneous identification of models and parameters of scientific simulators. - Cornelius Schröder; Jakob H. Macke (ICML, 2024)

Latent Diffusion for Neural Spiking Data. - Jaivardhan Kapoor; Auguste Schulz; Julius Vetter; Felix Pei; Richard Gao; Jakob H. Macke (NeurIPS, 2024)

Inferring stochastic low-rank recurrent neural networks from neural data. - Matthijs Pals; A Erdem Sagtekin; Felix Pei; Manuel Glöckler; Jakob H. Macke (NeurIPS, 2024)

Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation. - Julius Vetter; Guy Moss 001; Cornelius Schröder; Richard Gao; Jakob H. Macke (NeurIPS, 2024)

A Practical Guide to Statistical Distances for Evaluating Generative Models in Science. - Sebastian Bischoff 001; Alana Darcher; Michael Deistler; Richard Gao; Franziska Gerken; Manuel Glöckler; Lisa Haxel; Jaivardhan Kapoor; Janne K. Lappalainen; Jakob H. Macke; Guy Moss 001; Matthijs Pals; et al. (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)

Neural timescales from a computational perspective. - Roxana Zeraati; Anna Levina; Jakob H. Macke; Richard Gao (CoRR, 2024)

Generating realistic neurophysiological time series with denoising diffusion probabilistic models. - Julius Vetter; Jakob H. Macke; Richard Gao (Patterns, 2024)

A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science. - Sebastian Bischoff 001; Alana Darcher; Michael Deistler; Richard Gao; Franziska Gerken; Manuel Glöckler; Lisa Haxel; Jaivardhan Kapoor; Janne K. Lappalainen; Jakob H. Macke; Guy Moss 001; Matthijs Pals; et al. (Trans. Mach. Learn. Res., 2024)

Amortized Bayesian Decision Making for simulation-based models. - Mila Gorecki; Jakob H. Macke; Michael Deistler (Trans. Mach. Learn. Res., 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., 2024)

Compositional simulation-based inference for time series. - Manuel Glöckler; Shoji Toyota; Kenji Fukumizu; Jakob H. Macke (ICLR, 2025)

Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes. - J. P. Manzano-Patrón; Michael Deistler; Cornelius Schröder; Theodore Kypraios; Pedro J. Gonçalves; Jakob H. Macke; Stamatios N. Sotiropoulos (Medical Image Anal., 2025)

Personalized real-time inference of momentary excitability from human EEG. - Lisa Haxel; Oskari Ahola; Jaivardhan Kapoor; Ulf Ziemann; Jakob H. Macke (NeuroImage, 2025)

Combined statistical-biophysical modeling links ion channel genes to physiology of cortical neuron types. - Yves Bernaerts; Michael Deistler; Pedro J. Gonçalves; Jonas Beck; Marcel Stimberg; Federico Scala; Andreas S. Tolias; Jakob H. Macke; Dmitry Kobak; Philipp Berens (Patterns, 2025)

Real-time inference for binary neutron star mergers 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 (Nat., 2025)

Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models. - Julius Vetter; Manuel Glöckler; Daniel Gedon; Jakob H. Macke (CoRR, 2025)

FNOPE: Simulation-based inference on function spaces with Fourier Neural Operators. - Guy Moss 001; Leah Sophie Muhle; Reinhard Drews; Jakob H. Macke; Cornelius Schröder (CoRR, 2025)

sbi reloaded: a toolkit for simulation-based inference workflows. - Jan Boelts; Michael Deistler; Manuel Glöckler; Álvaro Tejero-Cantero; Jan-Matthis Lueckmann; Guy Moss 001; Peter Steinbach; Thomas Moreau 001; Fabio Muratore; Julia Linhart; Conor Durkan; Julius Vetter; et al. (J. Open Source Softw., 2025)

Learning Individual Reproductive Behavior from Aggregate Fertility Rates via Neural Posterior Estimation. - Daniel Ciganda; Ignacio Campón; Iñaki Permanyer; Jakob H. Macke (CoRR, 2025)

EDAPT: Towards Calibration-Free BCIs with Continual Online Adaptation. - Lisa Haxel; Jaivardhan Kapoor; Ulf Ziemann; Jakob H. Macke (CoRR, 2025)

Simulation-Based Inference: A Practical Guide. - Michael Deistler; Jan Boelts; Peter Steinbach; Guy Moss 001; Thomas Moreau 001; Manuel Glöckler; Pedro L. C. Rodrigues; Julia Linhart; Janne K. Lappalainen; Benjamin Kurt Miller; Pedro J. Gonçalves; Jan-Matthis Lueckmann; et al. (CoRR, 2025)

MRExtrap: Longitudinal Aging of Brain MRIs using Linear Modeling in Latent Space. - Jaivardhan Kapoor; Jakob H. Macke; Christian F. Baumgartner (CoRR, 2025)