Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Huai, Mengdi"'
This paper presents FedType, a simple yet pioneering framework designed to fill research gaps in heterogeneous model aggregation within federated learning (FL). FedType introduces small identical proxy models for clients, serving as agents for inform
Externí odkaz:
http://arxiv.org/abs/2407.03247
Autor:
Zhong, Yuan, Wang, Xiaochen, Wang, Jiaqi, Zhang, Xiaokun, Wang, Yaqing, Huai, Mengdi, Xiao, Cao, Ma, Fenglong
Synthesizing electronic health records (EHR) data has become a preferred strategy to address data scarcity, improve data quality, and model fairness in healthcare. However, existing approaches for EHR data generation predominantly rely on state-of-th
Externí odkaz:
http://arxiv.org/abs/2406.13942
In education data mining (EDM) communities, machine learning has achieved remarkable success in discovering patterns and structures to tackle educational challenges. Notably, fairness and algorithmic bias have gained attention in learning analytics o
Externí odkaz:
http://arxiv.org/abs/2405.16798
Despite the recent progress in deep neural networks (DNNs), it remains challenging to explain the predictions made by DNNs. Existing explanation methods for DNNs mainly focus on post-hoc explanations where another explanatory model is employed to pro
Externí odkaz:
http://arxiv.org/abs/2401.01549
Single domain generalization (SDG) aims to train a robust model against unknown target domain shifts using data from a single source domain. Data augmentation has been proven an effective approach to SDG. However, the utility of standard augmentation
Externí odkaz:
http://arxiv.org/abs/2312.12720
Vision Transformers (ViTs) have achieved state-of-the-art performance for various vision tasks. One reason behind the success lies in their ability to provide plausible innate explanations for the behavior of neural architectures. However, ViTs suffe
Externí odkaz:
http://arxiv.org/abs/2311.17983
Deep neural networks have exhibited remarkable performance across a wide range of real-world tasks. However, comprehending the underlying reasons for their effectiveness remains a challenging problem. Interpreting deep neural networks through examini
Externí odkaz:
http://arxiv.org/abs/2310.10708
Autor:
Zhong, Yuan, Cui, Suhan, Wang, Jiaqi, Wang, Xiaochen, Yin, Ziyi, Wang, Yaqing, Xiao, Houping, Huai, Mengdi, Wang, Ting, Ma, Fenglong
Health risk prediction is one of the fundamental tasks under predictive modeling in the medical domain, which aims to forecast the potential health risks that patients may face in the future using their historical Electronic Health Records (EHR). Res
Externí odkaz:
http://arxiv.org/abs/2310.02520
Diffusion models have begun to overshadow GANs and other generative models in industrial applications due to their superior image generation performance. The complex architecture of these models furnishes an extensive array of attack features. In lig
Externí odkaz:
http://arxiv.org/abs/2308.06405
As a way to implement the "right to be forgotten" in machine learning, \textit{machine unlearning} aims to completely remove the contributions and information of the samples to be deleted from a trained model without affecting the contributions of ot
Externí odkaz:
http://arxiv.org/abs/2304.03093