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Found 8 papers, 4 papers with code
Understanding Deep Generative Models with Generalized Empirical Likelihoods
Understanding how well a deep generative model captures a distribution of high-dimensional data remains an important open challenge.
GraphCast: Learning skillful medium-range global weather forecasting
Global medium-range weather forecasting is critical to decision-making across many social and economic domains.
Machine learning emulation of a local-scale UK climate model
This work demonstrates the effectiveness of diffusion models, a form of deep generative models, for generating much more cheaply realistic high resolution rainfall samples for the UK conditioned on data from a low resolution simulation.
Skillful Precipitation Nowcasting using Deep Generative Models of Radar
To address these challenges, we present a Deep Generative Model for the probabilistic nowcasting of precipitation from radar.
A review of radar-based nowcasting of precipitation and applicable machine learning techniques
A 'nowcast' is a type of weather forecast which makes predictions in the very short term, typically less than two hours - a period in which traditional numerical weather prediction can be limited.
Classification Accuracy Score for Conditional Generative Models
Deep generative models (DGMs) of images are now sufficiently mature that they produce nearly photorealistic samples and obtain scores similar to the data distribution on heuristics such as Frechet Inception Distance (FID).
Seeing is Not Necessarily Believing: Limitations of BigGANs for Data Augmentation
In fact, for one model in particular, BigGAN, metrics such as Inception Score or Frechet Inception Distance nearly match those of the dataset, suggesting that these models are close to match-ing the distribution of the training set.
Learning Implicit Generative Models with the Method of Learned Moments
We propose a method of moments (MoM) algorithm for training large-scale implicit generative models.
