Autoencoder with Ordered Variance for Nonlinear Model Identification
<|reference_start|>Autoencoder with Ordered Variance for Nonlinear Model Identification: This paper presents a novel autoencoder with ordered variance (AEO) in which the loss function is modified with a variance regularization term to enforce order in the latent space. Further, the autoencoder is modified using ResNets, which results in a ResNet AEO (RAEO). The paper also illustrates the effectiveness of AEO and RAEO in extracting nonlinear relationships among input variables in an unsupervised setting.<|reference_end|>
@article{augustine2024autoencoder,
    title={Autoencoder with Ordered Variance for Nonlinear Model Identification},
    author={Midhun T. Augustine, Parag Patil, Mani Bhushan, Sharad Bhartiya},
    journal={arXiv preprint arXiv:2402.14031},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14031},
    primaryClass={eess.SY cs.LG cs.SY}
}