Zobrazeno 1 - 10
of 280
pro vyhledávání: '"Shen, Zhiqi"'
Autor:
Li, Dongxu, Liu, Yudong, Wu, Haoning, Wang, Yue, Shen, Zhiqi, Qu, Bowen, Niu, Xinyao, Wang, Guoyin, Chen, Bei, Li, Junnan
Information comes in diverse modalities. Multimodal native AI models are essential to integrate real-world information and deliver comprehensive understanding. While proprietary multimodal native models exist, their lack of openness imposes obstacles
Externí odkaz:
http://arxiv.org/abs/2410.05993
Despite enjoying desirable efficiency and reduced reliance on domain expertise, existing neural methods for vehicle routing problems (VRPs) suffer from severe robustness issues -- their performance significantly deteriorates on clean instances with c
Externí odkaz:
http://arxiv.org/abs/2410.04968
Meta-learning has been widely used in recent years in areas such as few-shot learning and reinforcement learning. However, the questions of why and when it is better than other algorithms in few-shot classification remain to be explored. In this pape
Externí odkaz:
http://arxiv.org/abs/2410.02267
Federated Knowledge Graph Embedding (FKGE) aims to facilitate collaborative learning of entity and relation embeddings from distributed Knowledge Graphs (KGs) across multiple clients, while preserving data privacy. Training FKGE models with higher di
Externí odkaz:
http://arxiv.org/abs/2408.05748
Natural language counterfactual generation aims to minimally modify a given text such that the modified text will be classified into a different class. The generated counterfactuals provide insight into the reasoning behind a model's predictions by h
Externí odkaz:
http://arxiv.org/abs/2407.03993
Food recommendation systems serve as pivotal components in the realm of digital lifestyle services, designed to assist users in discovering recipes and food items that resonate with their unique dietary predilections. Typically, multi-modal descripti
Externí odkaz:
http://arxiv.org/abs/2406.18962
Federated Knowledge Graphs Embedding learning (FKGE) encounters challenges in communication efficiency stemming from the considerable size of parameters and extensive communication rounds. However, existing FKGE methods only focus on reducing communi
Externí odkaz:
http://arxiv.org/abs/2406.13225
Federated Knowledge Graph Embedding (FKGE) has recently garnered considerable interest due to its capacity to extract expressive representations from distributed knowledge graphs, while concurrently safeguarding the privacy of individual clients. Exi
Externí odkaz:
http://arxiv.org/abs/2406.11943
Autor:
Zhang, Honglei, Li, Haoxuan, Chen, Jundong, Cui, Sen, Yan, Kunda, Wuerkaixi, Abudukelimu, Zhou, Xin, Shen, Zhiqi, Li, Yidong
Federated recommendation aims to collect global knowledge by aggregating local models from massive devices, to provide recommendations while ensuring privacy. Current methods mainly leverage aggregation functions invented by federated vision communit
Externí odkaz:
http://arxiv.org/abs/2406.03933
Disease prediction holds considerable significance in modern healthcare, because of its crucial role in facilitating early intervention and implementing effective prevention measures. However, most recent disease prediction approaches heavily rely on
Externí odkaz:
http://arxiv.org/abs/2405.02935