| Bayesian deep learning for error estimation in the analysis of anomalous diffusion H Seckler, R Metzler Nature Communications 13 (1), 6717, 2022 | 57 | 2022 |
| Machine-learning solutions for the analysis of single-particle diffusion trajectories H Seckler, J Szwabinski, R Metzler The Journal of Physical Chemistry Letters 14 (35), 7910-7923, 2023 | 24 | 2023 |
| Directedeness, correlations, and daily cycles in springbok motion: From data via stochastic models to movement prediction PG Meyer, AG Cherstvy, H Seckler, R Hering, N Blaum, F Jeltsch, ... Physical Review Research 5 (4), 043129, 2023 | 18 | 2023 |
| Multifractal spectral features enhance classification of anomalous diffusion H Seckler, R Metzler, DG Kelty-Stephen, M Mangalam Physical Review E 109 (4), 044133, 2024 | 4 | 2024 |
| Machine-learning classification with additivity and diverse multifractal pathways in multiplicativity M Mangalam, H Seckler, DG Kelty-Stephen Physical Review Research 6 (3), 033276, 2024 | 3 | 2024 |
| Change-point detection in anomalous-diffusion trajectories utilising machine-learning-based uncertainty estimates H Seckler, R Metzler Journal of Physics: Photonics 6 (4), 045025, 2024 | 1 | 2024 |
| Bayesian deep learning for error estimation in the analysis of anomalous diffusion (vol 13, 6717, 2022) H Seckler, R Metzler NATURE COMMUNICATIONS 14 (1), 2023 | | 2023 |
| Author Correction: Bayesian deep learning for error estimation in the analysis of anomalous diffusion H Seckler, R Metzler Nature Communications 14 (1), 7876, 2023 | | 2023 |
