Federated learning is a decentralized and privacy-preserving technique t...
To improve the robustness of graph neural networks (GNN), graph structur...
Graph convolutional networks (GCN) are viewed as one of the most popular...
Learning-based video compression has been extensively studied over the p...
Federated learning is a distributed learning framework that takes full
a...
The rectified linear unit (ReLU) is a highly successful activation funct...
The importance of learning rate (LR) schedules on network pruning has be...
Mutual Information (MI) based feature selection makes use of MI to evalu...
Long-range time series forecasting is usually based on one of two existi...
Malicious attackers and an honest-but-curious server can steal private c...
Accurate long-range forecasting of time series data is an important prob...
We explore a new perspective on adapting the learning rate (LR) schedule...
Stein variational gradient descent (SVGD) and its variants have shown
pr...
Recent studies show that advanced priors play a major role in deep gener...
Vital signs including heart rate, respiratory rate, body temperature and...
Feature selection, which searches for the most representative features i...