Imperial College London
Alternate

Professor Anil Anthony Bharath

Faculty of Engineering, Department of Bioengineering

Academic Director (Singapore)
 
 
 
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Contact

 

+44 (0)20 7594 5463a.bharath Website

 
 
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Location

 

4.12Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Fotiadis:2023,
author = {Fotiadis, S and Lino, M and Hu, S and Garasto, S and Cantwell, CD and Bharath, AA},
pages = {10222--10248},
title = {Disentangled Generative Models for Robust Prediction of System Dynamics},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - The use of deep neural networks for modelling system dynamics is increasingly popular, but long-term prediction accuracy and out-of-distribution generalization still present challenges. In this study, we address these challenges by considering the parameters of dynamical systems as factors of variation of the data and leverage their ground-truth values to disentangle the representations learned by generative models. Our experimental results in phase-space and observation-space dynamics, demonstrate the effectiveness of latent-space supervision in producing disentangled representations, leading to improved long-term prediction accuracy and out-of-distribution robustness.
AU  - Fotiadis,S
AU  - Lino,M
AU  - Hu,S
AU  - Garasto,S
AU  - Cantwell,CD
AU  - Bharath,AA
EP  - 10248
PY  - 2023///
SP  - 10222
TI  - Disentangled Generative Models for Robust Prediction of System Dynamics
ER  -
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