During offline training, a mapping function is built between high and low resolution representations of a given design domain.
The existence of multiple datasets for sarcasm detection prompts us to apply transfer learning to exploit their commonality.
In this paper, we propose the FedHumor approach for the recognition of humorous content in a personalized manner through Federated Learning (FL).
Extensive experiments on classification and regression datasets demonstrate that DIFER can significantly improve the performance of various machine learning algorithms and outperform current state-of-the-art AutoFE methods in terms of both efficiency and performance.
We develop a new class of distribution--free multiple testing rules for false discovery rate (FDR) control under general dependence.
Methodology Statistics Theory Statistics Theory
According to the comprehensive qualitative analysis and quantitative comparison, ERSInvNet with tier feature map, smooth constraints, and depth weighting function together achieve the best performance.