How to design efficient algorithms is a central problem for reinforcement learning (RL).

A.1 Fullexperimentalresults In this section we provide the full experimental results that extend the results demonstrated in the Section 4.2. Table 8 demonstrates the evaluation on 16 robustly trained CIFAR10 models from RobustBench [28] that was summarized in the Table 2. We consider four configurations of the attack for each of the models. SA and AA correspond to the update size schedules proposed by Andriushchenko et al.[1]and Croce and Hein[2]respectively. "Uni" denotes sampling the color fortheupdateuniformly. A.2 Meta-trainingtheControllers The meta-training of controllers was described in Section 3 and Section 4.1.

Verification of neural networks enables us to gauge their robustness against adversarial attacks. Verification algorithms fall into two categories:exact verifiers that run in exponential time andrelaxed verifiers that are efficient but incomplete. In this paper, we unify all existing LP-relaxed verifiers, to the best of our knowledge, under a general convex relaxation framework.

The question of if and how rank collapse affects training is still largelyunanswered, anditsinvestigation isnecessary foramore comprehensive understanding ofthisarchitecture.

However,existing algorithms lack universality in the sense that they can only handle one type of convex functions and need apriori knowledge of parameters.

Existing deep learning based solutions typically restore the target layers individually, or with some concerns at the end of the output, barely taking into account the interaction across thetwostreams/branches. Inorder toutilize information more efficiently, this work presents a general yet simple interactive strategy, namely your trash is my treasure(YTMT), for constructing dual-stream decomposition networks.

In accelerate restricted 51, 50].

Thesemodels typically assume that all event sequences come from the same data distribution.

In this section, we ablate the kernel size used to train DISCO on ImageNet. TableIshowsthats=3 achieves the best performance, which degrades fors = 5 by a significant margin (3.26%). This is consistent with the well known complexity of synthesizing images withglobalmodels, suchasGANs. For a single ImageNet image of size 224, STL requires 23.71 seconds while DISCO (K=1) only requires0.027. In this section, we list the url links that are used for training and evaluating DISCO.

A common approach to this problem is to use a linear approximationVπ(s)=ϕ(s) x,whereϕ(s)isafeaturevectorofastate s.