Goto

Collaborating Authors

 Statistical Learning




80f2f15983422987ea30d77bb531be86-Paper.pdf

Neural Information Processing Systems

Wethenseparate theoptimization process into two steps, corresponding to weight update and structure parameter update. For the former step, we use the conventional chain rule, which can be sparse via exploiting the sparse structure.




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