In this paper, we consider the federated meta-learning problem of learning personalization strategies. Specifically, we consider meta-nets that induce the batch-norm and learning rate parameters for each client given local data statistics.

Now,since|N(v)|=β,itholds (Px)[v]= a+b 2, thus verifying the first claim of the lemma as the choice ofv was arbitrary. This construction essentially generalizes the graph demonstrated in Figure 1 of the main paper (see Sec. 7). The following lemma shows that onsuch graphs, the filter responses ofgθ for aconstant signal will encode some geometric information, butwill not distinguish between the cycles inthe graph. These responses with appropriate color coding give the illustration in Figure 1 in the main paper. Validation & testing procedure: All tests were done using train-validation-test splits of the datasets, where validation accuracy is used for tuning hyperparameters and test accuracy is reportedinthecomparisontable.

Here, we propose to augment conventional GCNs with geometric scattering transforms and residual convolutions. The former enables band-pass filtering of graph signals, thus alleviating the so-called oversmoothing often encountered in GCNs, while the latter is introduced to clear the resulting features of high-frequency noise.

In this paper, we study the finite-time convergence of nonlinear SA with multiplecoupledsequences.

In this paper, we study the finite-time convergence of nonlinear SA with multiplecoupledsequences.

When such backdoors exist, they allow the designer of the model to sell information on how to slightly perturb their input to change the outcome of the model. We develop a general strategy to plant backdoors to obfuscated neural networks, that satisfy the security properties of the celebrated notion of indistinguishability obfuscation. Applying obfuscation before releasing neural networks is a strategy that is well motivated to protect sensitive information of the external expert firm.