Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders

doi:doi:10.57702/6288keoq

Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders

Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator, with limited prior knowledge about the sources, and only access to a dataset of signal mixtures.

BibTex:

@dataset{Ali_Siahkoohi_and_Rudy_Morel_and_Randall_Balestriero_and_Erwan_Allys_and_Gregory_Sainton_and_Taichi_Kawamura_and_Maarten_V_de_Hoop_2024,
    abstract = {Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator, with limited prior knowledge about the sources, and only access to a dataset of signal mixtures.},
    author = {Ali Siahkoohi and Rudy Morel and Randall Balestriero and Erwan Allys and Gregory Sainton and Taichi Kawamura and Maarten V. de Hoop},
    doi = {10.57702/6288keoq},
    institution = {No Organization},
    keyword = {'multi-scale', 'nested approach', 'source separation', 'time series', 'variational autoencoders'},
    month = {dec},
    publisher = {TIB},
    title = {Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders},
    url = {https://service.tib.eu/ldmservice/dataset/martian-time-series-unraveled--a-multi-scale-nested-approach-with-factorial-variational-autoencoders},
    year = {2024}
}