A central challenge of representing natural signals, such as speech and music, is that they are structured across many different timescales (Chomsky and Halle, 1968; Lerdahl and Jackendoff, 1985; Hickokand Poeppel, 2007).

Neural processes (NPs) formulate exchangeable stochastic processes and are promising models for meta learning that do not require gradient updates during thetestingphase.

We tackle this planning issue by extending the policy gradient theory to policy updates with respecttoanystatedensity.

These works assume an arbitrary neural network with no prior knowledge of decoding algorithms, and accordingly, face the challenge of learning a decoding algorithm.

Social media platforms capture diverse attack sequence samples through both machine and manual screening processes. Investigating effective ways to leverage these adversarial samples to enhance robustness is imperative.