论文标题
带有训练的神经网络的贝叶斯推理
Bayesian Reasoning with Trained Neural Networks
论文作者
论文摘要
我们展示了如何使用训练有素的神经网络执行贝叶斯推理，以解决其最初范围之外的任务。深层生成模型提供了先验知识，分类/回归网络施加了约束。手头的任务是作为贝叶斯推理问题提出的，我们通过变异或采样技术大致解决了这一问题。该方法建立在已经训练有素的网络之上，可寻址问题随着可用网络的数量而超高地增长。该方法以最简单的形式产生条件生成模型。但是，多个同时约束构成了详尽的问题。我们比较了对经过专门训练的发电机的方法，展示了如何解决谜语，并证明了其与最先进的架构的兼容性。
We showed how to use trained neural networks to perform Bayesian reasoning in order to solve tasks outside their initial scope. Deep generative models provide prior knowledge, and classification/regression networks impose constraints. The tasks at hand were formulated as Bayesian inference problems, which we approximately solved through variational or sampling techniques. The approach built on top of already trained networks, and the addressable questions grew super-exponentially with the number of available networks. In its simplest form, the approach yielded conditional generative models. However, multiple simultaneous constraints constitute elaborate questions. We compared the approach to specifically trained generators, showed how to solve riddles, and demonstrated its compatibility with state-of-the-art architectures.
