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7608de7a475c0c878f60960d72a92654-Paper.pdf

Neural Information Processing Systems

Introspection reveals that our meta learned LAs learn through fast association in a way that is qualitatively different from gradientdescent.


RGMDT: Return-Gap-MinimizingDecisionTree ExtractioninNon-EuclideanMetricSpace

Neural Information Processing Systems

In this paper, we establish an upper bound on the return gap between the oracle expert policy and an optimal decision tree policy. This enables us to recast the DT extraction problem into a novel non-euclidean clustering problem over the local observation and action values space of each agent, with action values as cluster labels and the upper bound on the return gap as clustering loss.




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