518 citations · 598 across the 6 of their papers we have counts for
6 papers
Unsupervised Semantic Segmentation Facilitates Model Understanding
Xiaoyan Yu, Lisa Mais, Jannik Franzen +4
Self-supervised learning (SSL) has produced a diverse landscape of vision transformers (ViTs) whose pretrained representations support a wide range of downstream tasks. Towards a b…
Deeptime: a Python library for machine learning dynamical models from time series data
Moritz Hoffmann, Martin Scherer, Tim Hempel +8
Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics. In the physical sciences, structures such as metastab…
Progress in deep Markov State Modeling: Coarse graining and experimental data restraints
Andreas Mardt, Frank Noé
Recent advances in deep learning frameworks have established valuable tools for analyzing the long-timescale behavior of complex systems such as proteins. Especially the inclusion…
Deep learning Markov and Koopman models with physical constraints
Andreas Mardt, Luca Pasquali, Frank Noé +1
The long-timescale behavior of complex dynamical systems can be described by linear Markov or Koopman models in a suitable latent space. Recent variational approaches allow the lat…
Deep Generative Markov State Models
Hao Wu, Andreas Mardt, Luca Pasquali +1
We propose a deep generative Markov State Model (DeepGenMSM) learning framework for inference of metastable dynamical systems and prediction of trajectories. After unsupervised tra…
VAMPnets: Deep learning of molecular kinetics
Andreas Mardt, Luca Pasquali, Hao Wu +1
There is an increasing demand for computing the relevant structures, equilibria and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throu…