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Found 4 papers, 1 papers with code
Perturbing a Neural Network to Infer Effective Connectivity: Evidence from Synthetic EEG Data
Specifically, we trained neural networks (i. e., CNN, vanilla RNN, GRU, LSTM, and Transformer) to predict future EEG signals according to historical data and perturbed the networks' input to obtain effective connectivity (EC) between the perturbed EEG channel and the rest of the channels.
Exploiting Neighborhood Structural Features for Change Detection
In this letter, a novel method for change detection is proposed using neighborhood structure correlation.
FedGS: Federated Graph-based Sampling with Arbitrary Client Availability
Second, constrained by the far-distance in data distribution of the sampled clients, we further minimize the variance of the numbers of times that the clients are sampled, to mitigate long-term bias.
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs
Representation learning on temporal interaction graphs (TIG) is to model complex networks with the dynamic evolution of interactions arising in a broad spectrum of problems.
