Zobrazeno 1 - 10
of 318
pro vyhledávání: '"Meng, Xiangxu"'
Autor:
Ma, Haokai, Xie, Ruobing, Meng, Lei, Feng, Fuli, Du, Xiaoyu, Sun, Xingwu, Kang, Zhanhui, Meng, Xiangxu
Recommender systems aim to capture users' personalized preferences from the cast amount of user behaviors, making them pivotal in the era of information explosion. However, the presence of the dynamic preference, the "information cocoons", and the in
Externí odkaz:
http://arxiv.org/abs/2409.07237
Unifying Visual and Semantic Feature Spaces with Diffusion Models for Enhanced Cross-Modal Alignment
Image classification models often demonstrate unstable performance in real-world applications due to variations in image information, driven by differing visual perspectives of subject objects and lighting discrepancies. To mitigate these challenges,
Externí odkaz:
http://arxiv.org/abs/2407.18854
Federated learning benefits from cross-training strategies, which enables models to train on data from distinct sources to improve the generalization capability. However, the data heterogeneity between sources may lead models to gradually forget prev
Externí odkaz:
http://arxiv.org/abs/2405.20046
Representation learning for images has been advanced by recent progress in more complex neural models such as the Vision Transformers and new learning theories such as the structural causal models. However, these models mainly rely on the classificat
Externí odkaz:
http://arxiv.org/abs/2308.04142
Federated Learning aims to learn a global model on the server side that generalizes to all clients in a privacy-preserving manner, by leveraging the local models from different clients. Existing solutions focus on either regularizing the objective fu
Externí odkaz:
http://arxiv.org/abs/2308.03457
Multimedia recommendation aims to fuse the multi-modal information of items for feature enrichment to improve the recommendation performance. However, existing methods typically introduce multi-modal information based on collaborative information to
Externí odkaz:
http://arxiv.org/abs/2307.02761
Recently, significant advancements have been made in time-series forecasting research, with an increasing focus on analyzing the nature of time-series data, e.g, channel-independence (CI) and channel-dependence (CD), rather than solely focusing on de
Externí odkaz:
http://arxiv.org/abs/2305.04800
Causal inference has become a powerful tool to handle the out-of-distribution (OOD) generalization problem, which aims to extract the invariant features. However, conventional methods apply causal learners from multiple data splits, which may incur b
Externí odkaz:
http://arxiv.org/abs/2208.10156
Publikováno v:
In Computer Vision and Image Understanding August 2024 245
Publikováno v:
In Computer Vision and Image Understanding August 2024 245