Jakob H. Macke

101 papers A* 18A 1Journal 65Unranked 16

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Probabilistic Framework for LLM-Based Model Discovery. CoRR Stefan Wahl, Raphaela Schenk, Ali Farnoud, Jakob H. Macke, Daniel Gedon
2026	J	`jnl`	Scalable Simulation-Based Model Inference with Test-Time Complexity Control. CoRR Manuel Glöckler, J. P. Manzano-Patrón, Stamatios N. Sotiropoulos, Cornelius Schröder, Jakob H. Macke
2025	J	`jnl`	Combined statistical-biophysical modeling links ion channel genes to physiology of cortical neuron types. Patterns Yves Bernaerts, Michael Deistler, Pedro J. Gonçalves, Jonas Beck, Marcel Stimberg, Federico Scala, Andreas S. Tolias, Jakob H. Macke, Dmitry Kobak, Philipp Berens
2025	A*	`conf`	Compositional simulation-based inference for time series. ICLR Manuel Glöckler, Shoji Toyota, Kenji Fukumizu, Jakob H. Macke
2025	J	`jnl`	EDAPT: Towards Calibration-Free BCIs with Continual Online Adaptation. CoRR Lisa Haxel, Jaivardhan Kapoor, Ulf Ziemann, Jakob H. Macke
2025	J	`jnl`	Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models. CoRR Julius Vetter, Manuel Glöckler, Daniel Gedon, Jakob H. Macke
2025	J	`jnl`	FNOPE: Simulation-based inference on function spaces with Fourier Neural Operators. CoRR Guy Moss, Leah Sophie Muhle, Reinhard Drews, Jakob H. Macke, Cornelius Schröder
2025	J	`jnl`	Flexible Gravitational-Wave Parameter Estimation with Transformers. CoRR Annalena Kofler, Maximilian Dax, Stephen R. Green, Jonas Wildberger, Nihar Gupte, Jakob H. Macke, Jonathan Gair, Alessandra Buonanno, Bernhard Schölkopf
2025	J	`jnl`	Learning Individual Reproductive Behavior from Aggregate Fertility Rates via Neural Posterior Estimation. CoRR Daniel Ciganda, Ignacio Campón, Iñaki Permanyer, Jakob H. Macke
2025	J	`jnl`	MRExtrap: Longitudinal Aging of Brain MRIs using Linear Modeling in Latent Space. CoRR Jaivardhan Kapoor, Jakob H. Macke, Christian F. Baumgartner
2025	J	`jnl`	Personalized real-time inference of momentary excitability from human EEG. NeuroImage Lisa Haxel, Oskari Ahola, Jaivardhan Kapoor, Ulf Ziemann, Jakob H. Macke
2025	J	`jnl`	Real-time inference for binary neutron star mergers using machine learning. Nat. Maximilian Dax, Stephen R. Green, Jonathan Gair, Nihar Gupte, Michael Pürrer, Vivien Raymond, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf
2025	J	`jnl`	Simulation-Based Inference: A Practical Guide. CoRR Michael Deistler, Jan Boelts, Peter Steinbach, Guy Moss, Thomas Moreau, Manuel Glöckler, Pedro L. C. Rodrigues, Julia Linhart, Janne K. Lappalainen, Benjamin Kurt Miller, Pedro J. Gonçalves, Jan-Matthis Lueckmann, Cornelius Schröder, Jakob H. Macke
2025	J	`jnl`	Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes. Medical Image Anal. J. P. Manzano-Patrón, Michael Deistler, Cornelius Schröder, Theodore Kypraios, Pedro J. Gonçalves, Jakob H. Macke, Stamatios N. Sotiropoulos
2025	J	`jnl`	sbi reloaded: a toolkit for simulation-based inference workflows. J. Open Source Softw. Jan Boelts, Michael Deistler, Manuel Glöckler, Álvaro Tejero-Cantero, Jan-Matthis Lueckmann, Guy Moss, Peter Steinbach, Thomas Moreau, Fabio Muratore, Julia Linhart, Conor Durkan, Julius Vetter, Benjamin Kurt Miller, Maternus Herold, Abolfazl Ziaeemehr, Matthijs Pals, Theo Gruner, Sebastian Bischoff, Nastya Krouglova, Richard Gao, Janne K. Lappalainen, Bálint Mucsányi, Felix Pei, Auguste Schulz, Zinovia Stefanidi, Pedro Rodrigues, Cornelius Schröder, Faried Abu Zaid, Jonas Beck, Jaivardhan Kapoor, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke
2024	J	`jnl`	A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science. Trans. Mach. Learn. Res. 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
2024	J	`jnl`	A Practical Guide to Statistical Distances for Evaluating Generative Models in Science. CoRR 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
2024	A*	`conf`	All-in-one simulation-based inference. ICML Manuel Glöckler, Michael Deistler, Christian Dietrich Weilbach, Frank Wood, Jakob H. Macke
2024	J	`jnl`	All-in-one simulation-based inference. CoRR Manuel Glöckler, Michael Deistler, Christian Weilbach, Frank Wood, Jakob H. Macke
2024	J	`jnl`	Amortized Bayesian Decision Making for simulation-based models. Trans. Mach. Learn. Res. Mila Gorecki, Jakob H. Macke, Michael Deistler
2024	J	`jnl`	Compositional simulation-based inference for time series. CoRR Manuel Glöckler, Shoji Toyota, Kenji Fukumizu, Jakob H. Macke
2024	A*	`conf`	Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations. ICML Jonas Beck, Nathanael Bosch, Michael Deistler, Kyra L. Kadhim, Jakob H. Macke, Philipp Hennig, Philipp Berens
2024	J	`jnl`	Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations. CoRR Jonas Beck, Nathanael Bosch, Michael Deistler, Kyra L. Kadhim, Jakob H. Macke, Philipp Hennig, Philipp Berens
2024	J	`jnl`	Generating realistic neurophysiological time series with denoising diffusion probabilistic models. Patterns Julius Vetter, Jakob H. Macke, Richard Gao
2024	A*	`conf`	Inferring stochastic low-rank recurrent neural networks from neural data. NeurIPS Matthijs Pals, A Erdem Sagtekin, Felix Pei, Manuel Glöckler, Jakob H. Macke
2024	J	`jnl`	Inferring stochastic low-rank recurrent neural networks from neural data. CoRR Matthijs Pals, A Erdem Sagtekin, Felix Pei, Manuel Glöckler, Jakob H. Macke
2024	A*	`conf`	Latent Diffusion for Neural Spiking Data. NeurIPS Jaivardhan Kapoor, Auguste Schulz, Julius Vetter, Felix Pei, Richard Gao, Jakob H. Macke
2024	J	`jnl`	Latent Diffusion for Neural Spiking Data. CoRR Jaivardhan Kapoor, Auguste Schulz, Julius Vetter, Felix Pei, Richard Gao, Jakob H. Macke
2024	J	`jnl`	Neural timescales from a computational perspective. CoRR Roxana Zeraati, Anna Levina, Jakob H. Macke, Richard Gao
2024	J	`jnl`	Real-time gravitational-wave inference for binary neutron stars using machine learning. CoRR Maximilian Dax, Stephen R. Green, Jonathan Gair, Nihar Gupte, Michael Pürrer, Vivien Raymond, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf
2024	A*	`conf`	Simultaneous identification of models and parameters of scientific simulators. ICML Cornelius Schröder, Jakob H. Macke
2024	A*	`conf`	Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation. NeurIPS Julius Vetter, Guy Moss, Cornelius Schröder, Richard Gao, Jakob H. Macke
2024	J	`jnl`	Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation. CoRR Julius Vetter, Guy Moss, Cornelius Schröder, Richard Gao, Jakob H. Macke
2024	J	`jnl`	Trained recurrent neural networks develop phase-locked limit cycles in a working memory task. PLoS Comput. Biol. Matthijs Pals, Jakob H. Macke, Omri Barak
2024	J	`jnl`	sbi reloaded: a toolkit for simulation-based inference workflows. CoRR Jan Boelts, Michael Deistler, Manuel Glöckler, Álvaro Tejero-Cantero, Jan-Matthis Lueckmann, Guy Moss, Peter Steinbach, Thomas Moreau, Fabio Muratore, Julia Linhart, Conor Durkan, Julius Vetter, Benjamin Kurt Miller, Maternus Herold, Abolfazl Ziaeemehr, Matthijs Pals, Theo Gruner, Sebastian Bischoff, Nastya Krouglova, Richard Gao, Janne K. Lappalainen, Bálint Mucsányi, Felix Pei, Auguste Schulz, Zinovia Stefanidi, Pedro Rodrigues, Cornelius Schröder, Faried Abu Zaid, Jonas Beck, Jaivardhan Kapoor, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke
2023	A*	`conf`	Adversarial robustness of amortized Bayesian inference. ICML Manuel Glöckler, Michael Deistler, Jakob H. Macke
2023	J	`jnl`	Adversarial robustness of amortized Bayesian inference. CoRR Manuel Glöckler, Michael Deistler, Jakob H. Macke
2023	J	`jnl`	Amortized Bayesian Decision Making for simulation-based models. CoRR Mila Gorecki, Jakob H. Macke, Michael Deistler
2023	A*	`conf`	Flow Matching for Scalable Simulation-Based Inference. NeurIPS Jonas Wildberger, Maximilian Dax, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf
2023	J	`jnl`	Flow Matching for Scalable Simulation-Based Inference. CoRR Maximilian Dax, Jonas Wildberger, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf
2023	A*	`conf`	Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation. NeurIPS Richard Gao, Michael Deistler, Jakob H. Macke
2023	J	`jnl`	Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation. CoRR Richard Gao, Michael Deistler, Jakob H. Macke
2023	A*	`conf`	Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference. NeurIPS Basile Confavreux, Poornima Ramesh, Pedro J. Gonçalves, Jakob H. Macke, Tim P. Vogels
2023	J	`jnl`	Multiscale Metamorphic VAE for 3D Brain MRI Synthesis. CoRR Jaivardhan Kapoor, Jakob H. Macke, Christian F. Baumgartner
2023	J	`jnl`	Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layers. CoRR Guy Moss, Vjeran Visnjevic, Olaf Eisen, Falk M. Oraschewski, Cornelius Schröder, Jakob H. Macke, Reinhard Drews
2023	J	`jnl`	Simulation-based inference for efficient identification of generative models in computational connectomics. PLoS Comput. Biol. Jan Boelts, Philipp Harth, Richard Gao, Daniel Udvary, Felipe Yáñez, Daniel Baum, Hans-Christian Hege, Marcel Oberländer, Jakob H. Macke
2023	J	`jnl`	Simultaneous identification of models and parameters of scientific simulators. CoRR Cornelius Schröder, Jakob H. Macke
2022	J	`jnl`	Adapting to noise distribution shifts in flow-based gravitational-wave inference. CoRR Jonas Wildberger, Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf
2022	A*	`conf`	Efficient identification of informative features in simulation-based inference. NeurIPS Jonas Beck, Michael Deistler, Yves Bernaerts, Jakob H. Macke, Philipp Berens
2022	J	`jnl`	Efficient identification of informative features in simulation-based inference. CoRR Jonas Beck, Michael Deistler, Yves Bernaerts, Jakob H. Macke, Philipp Berens
2022	A*	`conf`	GATSBI: Generative Adversarial Training for Simulation-Based Inference. ICLR Poornima Ramesh, Jan-Matthis Lueckmann, Jan Boelts, Álvaro Tejero-Cantero, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke
2022	J	`jnl`	GATSBI: Generative Adversarial Training for Simulation-Based Inference. CoRR Poornima Ramesh, Jan-Matthis Lueckmann, Jan Boelts, Álvaro Tejero-Cantero, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke
2022	A*	`conf`	Group equivariant neural posterior estimation. ICLR Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, Jakob H. Macke
2022	J	`jnl`	Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference. CoRR Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf
2022	A*	`conf`	Truncated proposals for scalable and hassle-free simulation-based inference. NeurIPS Michael Deistler, Pedro J. Gonçalves, Jakob H. Macke
2022	J	`jnl`	Truncated proposals for scalable and hassle-free simulation-based inference. CoRR Michael Deistler, Pedro J. Gonçalves, Jakob H. Macke
2022	A*	`conf`	Variational methods for simulation-based inference. ICLR Manuel Glöckler, Michael Deistler, Jakob H. Macke
2022	J	`jnl`	Variational methods for simulation-based inference. CoRR Manuel Glöckler, Michael Deistler, Jakob H. Macke
2021	A	`conf`	Benchmarking Simulation-Based Inference. AISTATS Jan-Matthis Lueckmann, Jan Boelts, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke
2021	J	`jnl`	Benchmarking Simulation-Based Inference. CoRR Jan-Matthis Lueckmann, Jan Boelts, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke
2021	J	`jnl`	Group equivariant neural posterior estimation. CoRR Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, Jakob H. Macke
2021	J	`jnl`	Real-time gravitational-wave science with neural posterior estimation. CoRR Maximilian Dax, Stephen R. Green, Jonathan Gair, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf
2021	J	`jnl`	Simulation Intelligence: Towards a New Generation of Scientific Methods. CoRR 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
2020	J	`jnl`	Inference of a Mesoscopic Population Model from Population Spike Trains. Neural Comput. Alexandre René, André Longtin, Jakob H. Macke
2020	J	`jnl`	SBI - A toolkit for simulation-based inference. CoRR Álvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J. Gonçalves, David S. Greenberg, Jakob H. Macke
2020	J	`jnl`	sbi: A toolkit for simulation-based inference. J. Open Source Softw. Álvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J. Gonçalves, David S. Greenberg, Jakob H. Macke
2019	A*	`conf`	Automatic Posterior Transformation for Likelihood-Free Inference. ICML David S. Greenberg, Marcel Nonnenmacher, Jakob H. Macke
2019	J	`jnl`	Automatic Posterior Transformation for Likelihood-Free Inference. CoRR David S. Greenberg, Marcel Nonnenmacher, Jakob H. Macke
2019	J	`jnl`	Inference of a mesoscopic population model from population spike trains. CoRR Alexandre René, André Longtin, Jakob H. Macke
2019	A*	`conf`	Intrinsic dimension of data representations in deep neural networks. NeurIPS Alessio Ansuini, Alessandro Laio, Jakob H. Macke, Davide Zoccolan
2019	J	`jnl`	Intrinsic dimension of data representations in deep neural networks. CoRR Alessio Ansuini, Alessandro Laio, Jakob H. Macke, Davide Zoccolan
2019	J	`jnl`	Teaching deep neural networks to localize sources in super-resolution microscopy by combining simulation-based learning and unsupervised learning. CoRR Artur Speiser, Srinivas C. Turaga, Jakob H. Macke
2018	J	`jnl`	Analyzing biological and artificial neural networks: challenges with opportunities for synergy? CoRR David G. T. Barrett, Ari S. Morcos, Jakob H. Macke
2018	J	`jnl`	Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. PLoS Comput. Biol. 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
2018	—	`conf`	Likelihood-free inference with emulator networks. AABI Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos, Jakob H. Macke
2018	J	`jnl`	Likelihood-free inference with emulator networks. CoRR Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos, Jakob H. Macke
2017	—	`conf`	Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations. NIPS Marcel Nonnenmacher, Srinivas C. Turaga, Jakob H. Macke
2017	—	`conf`	Fast amortized inference of neural activity from calcium imaging data with variational autoencoders. NIPS Artur Speiser, Jinyao Yan, Evan W. Archer, Lars Buesing, Srinivas C. Turaga, Jakob H. Macke
2017	J	`jnl`	Fast amortized inference of neural activity from calcium imaging data with variational autoencoders. CoRR Artur Speiser, Jinyao Yan, Evan Archer, Lars Buesing, Srinivas C. Turaga, Jakob H. Macke
2017	—	`conf`	Flexible statistical inference for mechanistic models of neural dynamics. NIPS Jan-Matthis Lueckmann, Pedro J. Gonçalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, Jakob H. Macke
2017	J	`jnl`	Signatures of criticality arise from random subsampling in simple population models. PLoS Comput. Biol. Marcel Nonnenmacher, Christian Behrens, Philipp Berens, Matthias Bethge, Jakob H. Macke
2015	—	`conf`	Unlocking neural population non-stationarities using hierarchical dynamics models. NIPS Mijung Park, Gergo Bohner, Jakob H. Macke
2014	—	`conf`	A Bayesian model for identifying hierarchically organised states in neural population activity. NIPS Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke
2014	—	`ch.`	Electrophysiology Analysis, Bayesian. Encyclopedia of Computational Neuroscience Jakob H. Macke
2014	—	`conf`	Low-dimensional models of neural population activity in sensory cortical circuits. NIPS Evan W. Archer, Urs Köster, Jonathan W. Pillow, Jakob H. Macke
2013	J	`jnl`	Estimation Bias in Maximum Entropy Models. Entropy Jakob H. Macke, Iain Murray, Peter E. Latham
2013	—	`conf`	Inferring neural population dynamics from multiple partial recordings of the same neural circuit. NIPS Srinivas C. Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Häusser, Jakob H. Macke
2012	—	`conf`	Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. NIPS Lars Buesing, Jakob H. Macke, Maneesh Sahani
2011	—	`conf`	Empirical models of spiking in neural populations. NIPS Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
2011	J	`jnl`	Gaussian process methods for estimating cortical maps. NeuroImage Jakob H. Macke, Sebastian Gerwinn, Leonard E. White, Matthias Kaschube, Matthias Bethge
2011	—	`conf`	How biased are maximum entropy models? NIPS Jakob H. Macke, Iain Murray, Peter E. Latham
2011	J	`jnl`	Statistical Analysis of Multi-Cell Recordings: Linking Population Coding Models to Experimental Data. Frontiers Comput. Neurosci. Jakob H. Macke, Philipp Berens, Matthias Bethge
2010	J	`jnl`	Bayesian inference for generalized linear models for spiking neurons. Frontiers Comput. Neurosci. Sebastian Gerwinn, Jakob H. Macke, Matthias Bethge
2010	J	`jnl`	Modeling Population Spike Trains with Specified Time-Varying Spike Rates, Trial-to-Trial Variability, and Pairwise Signal and Noise Correlations. Frontiers Comput. Neurosci. Dmitry R. Lyamzin, Jakob H. Macke, Nicholas A. Lesica
2009	—	`conf`	Bayesian estimation of orientation preference maps. NIPS Jakob H. Macke, Sebastian Gerwinn, Leonard E. White, Matthias Kaschube, Matthias Bethge
2009	J	`jnl`	Bayesian population decoding of spiking neurons. Frontiers Comput. Neurosci. Sebastian Gerwinn, Jakob H. Macke, Matthias Bethge
2009	J	`jnl`	Generating Spike Trains with Specified Correlation Coefficients. Neural Comput. Jakob H. Macke, Philipp Berens, Alexander S. Ecker, Andreas S. Tolias, Matthias Bethge
2007	—	`conf`	Bayesian Inference for Spiking Neuron Models with a Sparsity Prior. NIPS Sebastian Gerwinn, Jakob H. Macke, Matthias W. Seeger, Matthias Bethge
2007	—	`conf`	Receptive Fields without Spike-Triggering. NIPS Jakob H. Macke, Guenther Zeck, Matthias Bethge
2007	—	`conf`	Unsupervised learning of a steerable basis for invariant image representations. Human Vision and Electronic Imaging Matthias Bethge, Sebastian Gerwinn, Jakob H. Macke
2006	—	`conf`	Inducing Metric Violations in Human Similarity Judgements. NIPS Julian Laub, Jakob H. Macke, Klaus-Robert Müller, Felix A. Wichmann