Thiscanbe formulated into anon-linear equality constrained optimization problem. Unlike GANs, solving such problem iscomputationally challenging, wethen proposed a simple yet effective procedure to decouple the alternating updates for the two networks for stability. By teaching the perturbation generator to hijacking the training trajectory of the victim classifier, the generator can thus learn to move against thevictim classifier stepbystep.

While existing automatic differentiation (AD) frameworks allow flexibly composing model architectures, theydonotprovide thesame flexibility forcomposing learning algorithms--everything has to be implemented in terms of backpropagation.

In our work, we propose the first distributed method with client sampling and provable tolerance to Byzantine workers.

To retrieve and recommend the most relevant items to users under response time limits, resorting to an efficient index structure is an effective and practical solution.

Asaresult, sinceeach 6 Table 1: results (", )-DPwith = 10 5. The? indicates representations.