Our experimental results demonstrate that the ranking algorithm-based classifiers provide plans that enhance infection rate control, especially with increased action budgets for small networks.

The success of contrastive learning is well known to be dependent on data augmentation.

First, we thank all the reviewers for their invaluable assessment of our paper in this challenging time. To provide more reliable evidence that AdvFlow's distributional For the sake of completeness, we also add LID [31] The results are given in Table 1. This is indicating that the attacker's distributional properties are fooling the detectors. As seen, we get similar results to Table 2 of the paper, outperforming SimBA in defended baselines. Note that some of the current SOT A results in black-box adversarial attacks come from the attacker's knowledge about the However, once the target changes its training procedure (e.g., from vanilla See the official repo. of SimBA, where it clearly is indicated that the The results of Table 1 and 2 (as well as SVHN) will be added to the camera-ready version.

The property of the received reward in this environment isset tobecollaborative. For each agent, we first sample rewardsˆri from estimated reward distributionsDi. NetworkArchitecture. Thedecentralized actors anddistributional rewardestimation networks adopt the simple fully-connected feedforward neural network with three layers in our framework. The two hidden layers' units are 64. The centralized critic uses a graph attention neural network with eight attention heads, and each head'shidden unit isset to8tocapture the dynamic relationship between agents.