This paper studies transfer-based attacks under a unified theoretical framework.

Based on our regularization, we propose an off-policy actor-critic algorithm.

We prove that when a PDE's coefficients are representable bysmall neural networks, theparameters required toapproximate its solution scale polynomially with the input dimensiond and proportionally to the parameter counts of the coefficient networks.

However,as already suggested, our model is not restricted to any specific intervention type or instantiation. Figure 1 (a) illustrates the performance of iSPN on theCausal Health data setfordifferent intervention types (perfect, atomic), noise terms (Gaussian, Gamma, Beta) and instantiations (Indicator Functions, Modifications). Nonetheless, it can be observed that some interventions are being modelled more precisely than others, e.g. ForEarthquakeand Cancer data sets, we use 5 different number ofsum node weights: 600, 1200, 1800, 2400 and3200. Forthesynthetic causal health data set we use 300, 600, 1000, 1500, 2000.

Haplotype assembly and viral quasispecies reconstruction are challenging tasks concerned with analysis of genomic mixtures using sequencing data.