Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Bai, Haoyue"'
Modern machine learning models deployed often encounter distribution shifts in real-world applications, manifesting as covariate or semantic out-of-distribution (OOD) shifts. These shifts give rise to challenges in OOD generalization and OOD detectio
Externí odkaz:
http://arxiv.org/abs/2410.08000
Out-of-distribution (OOD) learning often relies heavily on statistical approaches or predefined assumptions about OOD data distributions, hindering their efficacy in addressing multifaceted challenges of OOD generalization and OOD detection in real-w
Externí odkaz:
http://arxiv.org/abs/2408.07772
Multimedia recommendation, which incorporates various modalities (e.g., images, texts, etc.) into user or item representation to improve recommendation quality, has received widespread attention. Recent methods mainly focus on cross-modal alignment w
Externí odkaz:
http://arxiv.org/abs/2406.08270
Autor:
Cai, Miaomiao, Chen, Lei, Wang, Yifan, Bai, Haoyue, Sun, Peijie, Wu, Le, Zhang, Min, Wang, Meng
Collaborative Filtering (CF) typically suffers from the significant challenge of popularity bias due to the uneven distribution of items in real-world datasets. This bias leads to a significant accuracy gap between popular and unpopular items. It not
Externí odkaz:
http://arxiv.org/abs/2405.20718
Autor:
He, Zhuangzhuang, Wang, Yifan, Yang, Yonghui, Sun, Peijie, Wu, Le, Bai, Haoyue, Gong, Jinqi, Hong, Richang, Zhang, Min
As its availability and generality in online services, implicit feedback is more commonly used in recommender systems. However, implicit feedback usually presents noisy samples in real-world recommendation scenarios (such as misclicks or non-preferen
Externí odkaz:
http://arxiv.org/abs/2405.11272
Autor:
Bai, Haoyue, Wu, Le, Hou, Min, Cai, Miaomiao, He, Zhuangzhuang, Zhou, Yuyang, Hong, Richang, Wang, Meng
Multimedia-based recommendation provides personalized item suggestions by learning the content preferences of users. With the proliferation of digital devices and APPs, a huge number of new items are created rapidly over time. How to quickly provide
Externí odkaz:
http://arxiv.org/abs/2405.15783
Out-of-distribution (OOD) generalization is critical for machine learning models deployed in the real world. However, achieving this can be fundamentally challenging, as it requires the ability to learn invariant features across different domains or
Externí odkaz:
http://arxiv.org/abs/2402.07785
This paper considers image change detection with only a small number of samples, which is a significant problem in terms of a few annotations available. A major impediment of image change detection task is the lack of large annotated datasets coverin
Externí odkaz:
http://arxiv.org/abs/2311.03762
Deep neural networks achieve superior performance for learning from independent and identically distributed (i.i.d.) data. However, their performance deteriorates significantly when handling out-of-distribution (OoD) data, where the training and test
Externí odkaz:
http://arxiv.org/abs/2307.12219
Modern machine learning models deployed in the wild can encounter both covariate and semantic shifts, giving rise to the problems of out-of-distribution (OOD) generalization and OOD detection respectively. While both problems have received significan
Externí odkaz:
http://arxiv.org/abs/2306.09158