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Jakob Macke
Jakob Macke
Machine Learning in Science, Tübingen University
Verified email at uni-tuebingen.de - Homepage
Title
Cited by
Cited by
Year
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
3102016
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
2552011
Generating spike trains with specified correlation coefficients
JH Macke, P Berens, AS Ecker, AS Tolias, M Bethge
Neural Computation 21 (2), 397-423, 2009
2342009
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
228*2019
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
2232015
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
2022015
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
1952017
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
1942019
Quantifying the effect of intertrial dependence on perceptual decisions
I Fründ, FA Wichmann, JH Macke
Journal of vision 14 (7), 9-9, 2014
1902014
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
1712013
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
1522020
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
1422020
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
1282018
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
1242021
Benchmarking Simulation-Based Inference
JM Lueckmann, J Boelts, D Greenberg, P Goncalves, JH Macke
International Conference on Artificial Intelligence and Statistics, 343-351, 2021
1222021
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
1202021
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
1142011
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
111*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
1092019
Bayesian inference for generalized linear models for spiking neurons
S Gerwinn, JH Macke, M Bethge
Frontiers in computational neuroscience 4, 2010
812010
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Articles 1–20