All models discussed in the paper use ReLU non-linearity.

Recurrent Neural Networks (RNNs) are effective tools for processing sequential data.

Once closed-form expressions for these Jacobians are derived, it remains to substitute those expressions into (16). The following identity (often termed the "vec" rule) will To depict the spatial topographies of the latent components measured on the EEG and fMRI analyses, the "forward-model" [ The results of the comparison are shown in Fig S1, where it is clear that the signal fidelity of the GCs (right panel) significantly exceeds those yielded by PCA (left) and ICA (middle). GCA is only able to recover sources with temporal dependencies (i.e., s Both the single electrodes and Granger components exhibit two pronounced peaks in the spectra: one near 2 Hz ("delta" Fig S3 shows the corresponding result for the left motor imagery condition. EEG motor imagery dataset described in the main text. For each technique, the first 6 components are presented.

RL problems through the idea of "learning to learn". Current meta-RL methods can be classified in to two categories. These methods mainly differ in their ways of inference [3, 4, 20]. The other line follows the technique of relabeling that enables sample reuse across tasks, i.e., learning a task Packer et al. apply hindsight relabeling for meta-RL, and propose hindsight task relabeling (HTR) to relabel the trajectories Taking a step further than hindsight relabelling, Wan et al. introduce additionally foresight Huang et al. derive a general form of policy gradient from DR value estimator [29], whereas a DR off-policy actor-critic Kallus et al. propose the doubly robust method to find a robust policy that can Depending on the knowledge to be transferred, these methods in RL can be roughly divided into classes including sampled transitions [32, 33], learned policies or value networks [34, 35, 36, 37], features [38, 39, 40], and skills [41, 42]. Doubly Robust Property for Direct Use of Doubly Robust Estimator We show the doubly robust property of the DR estimator for value function in Eq. (5) in the main text, as follows.

Privacy and communication constraints are two major bottlenecks in federated learning (FL) and analytics (FA).