| Interacting particle solutions of Fokker–Planck equations through gradient–log–density estimation D Maoutsa, S Reich, M Opper Entropy 22 (8), 802, 2020 | 61 | 2020 |
| Brainhack: Developing a culture of open, inclusive, community-driven neuroscience R Gau, S Noble, K Heuer, KL Bottenhorn, IP Bilgin, YF Yang, ... Neuron, 2021 | 34 | 2021 |
| Inferring network connectivity from event timing patterns J Casadiego*, D Maoutsa*, M Timme Physical review letters 121 (5), 054101, 2018 | 27 | 2018 |
| Deterministic particle flows for constraining stochastic nonlinear systems D Maoutsa, M Opper Physical Review Research 4 (4), 2022 | 9 | 2022 |
| Deterministic particle flows for constraining SDEs D Maoutsa, M Opper Machine Learning and the Physical Sciences, Workshop at the 35th Conference …, 2021 | 3 | 2021 |
| Geometric constraints improve inference of sparsely observed stochastic dynamics D Maoutsa International Conference on Learning Representations (ICLR) 2023 -- Workshop …, 2023 | 2 | 2023 |
| Revealing latent stochastic dynamics from single-trial spike train observations D Maoutsa Bernstein Conference for Computational Neuroscience 2022, https://doi.org/10 …, 2022 | 2 | 2022 |
| Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems D Maoutsa Neural Information Processing Systems (NeurIPS 2022) - - Machine Learning …, 2022 | 1 | 2022 |
| Discovering latent dynamical laws from neural population responses D Maoutsa Bernstein Conference for Computational Neuroscience 2023, 2023 | | 2023 |
| Inference of latent and sparsely observed stochastic dynamics via stochastic control D Maoutsa Recent advances in understanding Artificial and Biological Neural Networks …, 2023 | | 2023 |
| Deterministic particle flows for stochastic nonlinear systems: Simulation, Control, and Inference DD Maoutsa Technical University of Berlin, 2023 | | 2023 |
| Stochastic optimal control from deterministic particle flows D Maoutsa, M Opper Poster - Isaac Newton Institute - MDLW03 workshop, 2021 | | 2021 |
| Revealing network physical interactions from event timing patterns: A model-free approach D Maoutsa Workshop on Stochastic dynamics on large networks: Prediction and inference …, 2018 | | 2018 |
| Recovering stochastic systems from discrete time observations: A variational approach D Maoutsa, M Opper Workshop on Stochastic dynamics on large networks: Prediction and inference …, 2018 | | 2018 |
| Phase Transitions in Autonomous Intersection Traffic? D Maoutsa, D Manik, M Schröder, M Timme Deutsche Physikalische Gesellschaft Frühjahrstagung (DPG) 2017 - German …, 2017 | | 2017 |
| Model-free reconstruction of synaptic connectivity from spike trains D Maoutsa Max Planck Institute for Dynamics and Self-Organisation & Georg-August …, 2016 | | 2016 |
| Connectomics through dynamics: revealing synaptic connectivity from spikes J Casadiego, D Maoutsa, M Timme MPIDS Research Report 2016, 149-150, 2016 | | 2016 |
| Connectomics through nonlinear dynamics? J Casadiego*, D Maoutsa*, M Timme https://doi.org/10.12751/NNCN.BC2016.0054, 2016 | | 2016 |
| Geometric path augmentation for inference of sparsely observed stochastic nonlinear systems D Maoutsa | | |
