Safeguarding privacy in sensitive training data is paramount, particularly in the context of generative modeling.
Modeling large scenes from unconstrained images has proven to be a major challenge in computer vision.
Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance.
Effective data-driven PDE forecasting methods often rely on fixed spatial and / or temporal discretizations.
A recent line of work in the machine learning community addresses the problem of predicting high-dimensional spatiotemporal phenomena by leveraging specific tools from the differential equations theory.
Designing video prediction models that account for the inherent uncertainty of the future is challenging.
Ranked #1 on Video Prediction on Cityscapes 128x128 (Pred metric)
Time series constitute a challenging data type for machine learning algorithms, due to their highly variable lengths and sparse labeling in practice.