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Found 4 papers, 1 papers with code
Federated Learning based on Pruning and Recovery
A novel federated learning training framework for heterogeneous environments is presented, taking into account the diverse network speeds of clients in realistic settings.
Topic model based on co-occurrence word networks for unbalanced short text datasets
We propose a straightforward solution for detecting scarce topics in unbalanced short-text datasets.
Federated Topic Model and Model Pruning Based on Variational Autoencoder
The first method involves slow pruning throughout the entire model training process, which has limited acceleration effect on the model training process, but can ensure that the pruned model achieves higher accuracy.
A Rare Topic Discovery Model for Short Texts Based on Co-occurrence word Network
We provide a simple and general solution for the discovery of scarce topics in unbalanced short-text datasets, namely, a word co-occurrence network-based model CWIBTD, which can simultaneously address the sparsity and unbalance of short-text topics and attenuate the effect of occasional pairwise occurrences of words, allowing the model to focus more on the discovery of scarce topics.
