| 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 | 393 | 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 | 352* | 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 | 306 | 2020 |
| 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 | 302 | 2019 |
| 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 | 301 | 2011 |
| 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 | 298 | 2017 |
| 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 | 261 | 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 | 246 | 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 | 242 | 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 | 233 | 2009 |
| 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 | 220 | 2015 |
| Benchmarking Simulation-Based Inference JM Lueckmann, J Boelts, D Greenberg, P Goncalves, JH Macke International Conference on Artificial Intelligence and Statistics, 343-351, 2021 | 217 | 2021 |
| Quantifying the effect of intertrial dependence on perceptual decisions I Fründ, FA Wichmann, JH Macke Journal of vision 14 (7), 9-9, 2014 | 215 | 2014 |
| 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 | 214 | 2021 |
| 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 | 153 | 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 | 144* | 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 | 140 | 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 | 134 | 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 |
Pedro J. GoncalvesVIB-Neuroelectronics Research Flanders (NERF)確認したメール アドレス: nerf.be
