Jakob Macke

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Pedro J. GoncalvesVIB-Neuroelectronics Research Flanders (NERF)Verifierad e-postadress på nerf.be
Michael DeistlerTübingen UniversityVerifierad e-postadress på uni-tuebingen.de
Jan-Matthis LueckmannGoogleVerifierad e-postadress på jan-matthis.de
Matthias BethgeTübingen University & Maddox Co-FounderVerifierad e-postadress på bethgelab.org
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerifierad e-postadress på tuebingen.mpg.de
David S GreenbergHelmholz Centre HereonVerifierad e-postadress på hereon.de
Jan BoeltsTransferLab - appliedAI Institute for EuropeVerifierad e-postadress på appliedai-institute.de
Giacomo Bassettoresearch center caesar, an associate of the Max Planck Society, Bonn, GermanyVerifierad e-postadress på caesar.de
Stephen R GreenUniversity of NottinghamVerifierad e-postadress på nottingham.ac.uk
Maximilian DaxPostdoctoral Researcher at ETH ZurichVerifierad e-postadress på inf.ethz.ch
Marcel NonnenmacherUniversity College LondonVerifierad e-postadress på ucl.ac.uk
Srinivas C. TuragaHHMI Janelia Research CampusVerifierad e-postadress på janelia.hhmi.org
Felix WichmannEberhard Karls Universität TübingenVerifierad e-postadress på uni-tuebingen.de
Lars BuesingGoogle DeepMindVerifierad e-postadress på google.com
Alessandra BuonannoDirector, Max Planck Institute for Gravitational Physics, Potsdam; Professor, University of MarylandVerifierad e-postadress på aei.mpg.de
Philipp BerensHertie Institute for AI in Brain Health, University of TübingenVerifierad e-postadress på uni-tuebingen.de
Maneesh SahaniGatsby Unit, UCLVerifierad e-postadress på gatsby.ucl.ac.uk
Álvaro Tejero-CanteroPenpot (Kaleidos Ventures)Verifierad e-postadress på teje.ro
Poornima RameshStudent, University of TuebingenVerifierad e-postadress på uni-tuebingen.de
Tim P. VogelsInstitute of Science and Technology, AustriaVerifierad e-postadress på ist.ac.at

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Jakob Macke

Machine Learning in Science, Tübingen University

Verifierad e-postadress på uni-tuebingen.de - Startsida

Machine learning AI for Science NeuroAI Computational neuroscience Simulation-based inference


Titel Sortera efter citat Sortera efter år Sortera efter titel	Citeras av Citeras av	År
Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data HH Schütt, S Harmeling, JH Macke, FA Wichmann Vision research 122, 105-123, 2016	395	2016
Automatic Posterior Transformation for Likelihood-Free Inference Greenberg D. S., Nonnenmacher M., Macke J. H. Proceedings of the 36th International Conference on Machine Learning, ICML …, 2019	361*	2019
Intrinsic dimension of data representations in deep neural networks A Ansuini, A Laio, JH Macke, Z D Advances in Neural Information Processing Systems 32 (Neurips 2019), 2019	314	2019
sbi: A toolkit for simulation-based inference A Tejero-Cantero, J Boelts, M Deistler, JM Lueckmann, C Durkan, ... Journal of Open Source Software 5 (52), 2505, 2020	309	2020
Flexible statistical inference for mechanistic models of neural dynamics JM Lueckmann, PJ Goncalves, G Bassetto, K Öcal, M Nonnenmacher, ... Advances in neural information processing systems 30, 2017	309	2017
Empirical models of spiking in neural populations JH Macke, L Buesing, JP Cunningham, BM Yu, KV Shenoy, M Sahani Advances in Neural Information Processing Systems 24, 2011	300	2011
Neural population coding: combining insights from microscopic and mass signals S Panzeri, JH Macke, J Gross, C Kayser Trends in cognitive sciences 19 (3), 162-172, 2015	265	2015
Real-time gravitational wave science with neural posterior estimation M Dax, SR Green, J Gair, JH Macke, A Buonanno, B Schölkopf Physical review letters 127 (24), 241103, 2021	252	2021
Training deep neural density estimators to identify mechanistic models of neural dynamics PJ Gonçalves, JM Lueckmann, M Deistler, M Nonnenmacher, K Öcal, ... elife 9 (e56261), 2020	249	2020
Generating spike trains with specified correlation coefficients JH Macke, P Berens, AS Ecker, AS Tolias, M Bethge Neural Computation 21 (2), 397-423, 2009	234	2009
Benchmarking Simulation-Based Inference JM Lueckmann, J Boelts, D Greenberg, P Goncalves, JH Macke International Conference on Artificial Intelligence and Statistics, 343-351, 2021	224	2021
Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression R Küffner, N Zach, R Norel, J Hawe, D Schoenfeld, L Wang, G Li, L Fang, ... Nature biotechnology 33 (1), 51-57, 2015	222	2015
Deep learning enables fast and dense single-molecule localization with high accuracy A Speiser, LR Müller, P Hoess, U Matti, CJ Obara, WR Legant, A Kreshuk, ... Nature methods 18 (9), 1082-1090, 2021	221	2021
Quantifying the effect of intertrial dependence on perceptual decisions I Fründ, FA Wichmann, JH Macke Journal of vision 14 (7), 9-9, 2014	218	2014
Inferring decoding strategies from choice probabilities in the presence of correlated variability RM Haefner, S Gerwinn, JH Macke, M Bethge Nature neuroscience 16 (2), 235-242, 2013	182	2013
Community-based benchmarking improves spike rate inference from two-photon calcium imaging data P Berens, J Freeman, T Deneux, N Chenkov, T McColgan, A Speiser, ... PLoS computational biology 14 (5), e1006157, 2018	159	2018
Likelihood-free inference with emulator networks JHM Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos Proceedings of The 1st Symposium on Advances in Approximate Bayesian …, 2019	148*	2019
Analyzing biological and artificial neural networks: challenges with opportunities for synergy? DGT Barrett, AS Morcos, JH Macke Current opinion in neurobiology 55, 55-64, 2019	141	2019
Simulation Intelligence: Towards a New Generation of Scientific Methods A Lavin, H Zenil, B Paige, D Krakauer, J Gottschlich, T Mattson, ... arXiv preprint arXiv:2112.03235, 2021	133	2021
Common Input Explains Higher-Order Correlations and Entropy in a Simple Model of Neural Population Activity JH Macke, M Opper, M Bethge Physical Review Letters 106 (20), 208102, 2011	115	2011

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