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Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models
Generative modeling via stochastic processes has led to remarkable empirical results as well as to recent advances in their theoretical understanding.
Fast and Unified Path Gradient Estimators for Normalizing Flows
Recent work shows that path gradient estimators for normalizing flows have lower variance compared to standard estimators for variational inference, resulting in improved training.
Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks
Molecular dynamics simulations are a cornerstone in science, allowing to investigate from the system's thermodynamics to analyse intricate molecular interactions.
Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology
The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications, as the risk of model degradation in a changing environment is eminent.
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