Existing permanental processes often impose constraints on kernel types or sta-tionarity, limiting the model's expressiveness. To overcome these limitations, we propose a novel approach utilizing the sparse spectral representation of non-stationary kernels.

Camera-based physiological measurement is a fast growing field of computer vision.

The SI2E analyzes value differences in the agent's policy between state-action

DSN primarily seeks to transfer knowledge to the new coming task from the learned tasks by selecting the affiliated weights of a small set of neurons to be activated, including the reused neurons from prior tasks via neuron-wise masks. And it also transfers possibly valuable knowledge to the earlier tasks via data-free replay.

Lemma 3. Consider the ReLU activation The proof of Theorem 1 is given below. The inequality 3 uses strictly monotone property of p () . Code is available at this link. The neural networks are updated using Adam with learning rate initializes at 0.035 and All of them have no communication constraints. The training time is shown in Table 1.