1 code implementation • 16 Mar 2024 • Chengjie Ma
A novel federated learning training framework for heterogeneous environments is presented, taking into account the diverse network speeds of clients in realistic settings.
no code implementations • 5 Nov 2023 • Chengjie Ma, Junping Du, Meiyu Liang, Zeli Guan
We propose a straightforward solution for detecting scarce topics in unbalanced short-text datasets.
no code implementations • 1 Nov 2023 • Chengjie Ma, Yawen Li, Meiyu Liang, Ang Li
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.
no code implementations • 30 Jun 2022 • Chengjie Ma, Junping Du, Yingxia Shao, Ang Li, Zeli Guan
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.