Does Symbolic Knowledge Prevent Adversarial Fooling?
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Arguments in favor of injecting symbolic knowledge into neural architectures abound. When done right, constraining a sub-symbolic model can substantially improve its performance and sample complexity and prevent it from predicting invalid configurations. Focusing on deep probabilistic (logical) graphical models – i.e., constrained joint distributions whose parameters are determined (in part) by neural nets based on low-level inputs – we draw attention to an elementary but unintended consequence of symbolic knowledge: that the resulting constraints can propagate the negative effects of adversarial examples.
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