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1,584 claims from 981 papers are on the record. 46 have been checked so far; the other 1,538 have no check with a result yet.

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Status: Unchecked ×Keyword: variational autoencoder ×Clear all

7 claims from 3 papers

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   SwinVRNN: A Data‐Driven Ensemble Forecasting Model via Learned Distribution Perturbation

   Hu, Chen, Wang and Li · Journal of Advances in Modeling Earth Systems · 2023

   The paper presents SwinVRNN, a data-driven weather model that learns its own perturbation distribution for ensemble forecasts, and compares it with other perturbation methods on WeatherBench.

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   1. UncheckedOn WeatherBench, learning the noise distribution in SwinVRNN gave better forecast accuracy and a reasonable ensemble spread than the other perturbation methods compared.“Comparisons on WeatherBench dataset show the learned distribution perturbation method using our SwinVRNN model achieves superior forecast accuracy and reasonable ensemble spread due to joint optimization of the two targets.”
   2. UncheckedOn the WeatherBench dataset, SwinVRNN is reported to beat the operational IFS on 2-m temperature and 6-hourly rainfall at every lead time up to five days.“More notably, SwinVRNN surpasses operational IFS on surface variables of 2-m temperature and 6-hourly total precipitation at all lead times up to five days.”

2. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   FuXi-ENS: A machine learning model for efficient and accurate ensemble weather prediction

   Zhong, Chen, Li et al. · Science Advances · 2025

   The paper introduces FuXi-ENS, a machine learning model producing 6-hourly global ensemble forecasts up to 15 days ahead at 0.25° resolution, and reports that it outperforms the ECMWF ensemble on key metrics.

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   1. UncheckedFuXi-ENS, a machine learning ensemble model, is reported to beat the ECMWF ensemble on key forecast scores such as CRPS and Brier score.“Comprehensive evaluations demonstrate that FuXi-ENS outperforms the ECMWF ensemble in key forecast metrics such as CRPS and Brier score.”
   2. UncheckedFuXi-ENS uses a variational autoencoder trained on a loss combining CRPS and Kullback-Leibler divergence, which lets it make flow-dependent perturbations.“Using a variational autoencoder framework, FuXi-ENS optimizes a loss function that combines the continuous ranked probability score (CRPS) with the Kullback-Leibler divergence, enabling flow-dependent perturbations.”

3. Computer Science › Advanced Neural Network Applications

   Winning Lottery Tickets in Deep Generative Models

   Kalibhat, Balaji and Feizi · arXiv (Cornell University) · 2020

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   1. Unchecked“This approach effectively yields tickets with sparsity up to 99% for AutoEncoders, 93% for VAEs and 89% for GANs on CIFAR and Celeb-A datasets.”
   2. Unchecked“We also demonstrate the transferability of winning tickets across different generative models (GANs and VAEs) sharing the same architecture, suggesting that winning tickets have inductive biases that could help train a wide range of deep generative models.”
   3. Unchecked“Through early-bird tickets, we can achieve up to 88% reduction in floating-point operations (FLOPs) and 54% reduction in training time, making it possible to train large-scale generative models over tight resource constraints.”