LatentTSF improves time series forecasting accuracy and representation quality by shifting prediction from observation space to a learned latent state space via autoencoding.
Voloshynovskiy al.Information bottleneck through variational glasses
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A kernel density estimator model trained on pion test beam data produces fast simulations of AHCAL showers that match measured observables and can be interpolated to arbitrary energies.
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From Observations to States: Latent Time Series Forecasting
LatentTSF improves time series forecasting accuracy and representation quality by shifting prediction from observation space to a learned latent state space via autoencoding.
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An investigation of fast simulation techniques for pion showers using kernel density estimators with the CALICE AHCAL Technological Prototype
A kernel density estimator model trained on pion test beam data produces fast simulations of AHCAL showers that match measured observables and can be interpolated to arbitrary energies.