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 Statistical Learning



79121bb953a3bd47c076f20234bafd2e-Supplemental.pdf

Neural Information Processing Systems

Inrecentyears,quantitativestatistical and algorithmic treatments of these formulations have produced insights into modern computational methods-resulting innovelapproaches todifficult, open problems.




Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network

Neural Information Processing Systems

Among potential attacks against FR systems [Galbally et al., 2014, Biggio et al., 2015, Hadid et al., 2015, Mai et al., 2018, Marcel et al., 2023], the template inversion (TI) attack significantly jeopardizes the users' privacy. In a TI attack, the adversary gains access to templates stored in the FR system's database and aims



6f5216f8d89b086c18298e043bfe48ed-Paper.pdf

Neural Information Processing Systems

Withoutrequiring repeatable trials, itcanflexibly capture covariate-dependent jointSCDs, andprovide interpretable latent causes underlying the statistical dependencies between neurons.