45 citations · 105 across the 10 of their papers we have counts for
4 papers · 1 filter
Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance
Dun Zeng, Zenglin Xu, Yu Pan +3
Federated Learning (FL) is a distributed learning paradigm to train a global model across multiple devices without collecting local data. In FL, a server typically selects a subset…
Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning
Zhipeng Zhang, Piao Tong, Yingwei Ma +3
Session-based recommendation techniques aim to capture dynamic user behavior by analyzing past interactions. However, existing methods heavily rely on historical item ID sequences…
DETA: Denoised Task Adaptation for Few-Shot Learning
Ji Zhang, Lianli Gao, Xu Luo +2
Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic model for capturing taskspecific knowledge of the test task, rely only on few-labeled suppo…
A Closer Look at Few-shot Classification Again
Xu Luo, Hao Wu, Ji Zhang +3
Few-shot classification consists of a training phase where a model is learned on a relatively large dataset and an adaptation phase where the learned model is adapted to previously…