Zobrazeno 1 - 10
of 9 908
pro vyhledávání: '"Fuli An"'
Autor:
Bai, Xudong, Wang, Longpan, Chen, Yuhua, Lu, Xilong, Zhang, Fuli, Chen, Jingfeng, Chen, Wen, Xu, He-Xiu
Programmable metasurfaces promise a great potential to construct low-cost phased array systems due to the capability of elaborate modulation over electromagnetic (EM) waves. However, they are in either reflective or transmissive mode, and usually pos
Externí odkaz:
http://arxiv.org/abs/2412.04822
Frequently updating Large Language Model (LLM)-based recommender systems to adapt to new user interests -- as done for traditional ones -- is impractical due to high training costs, even with acceleration methods. This work explores adapting to dynam
Externí odkaz:
http://arxiv.org/abs/2410.23136
MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding
Autor:
Zhu, Fengbin, Liu, Ziyang, Ng, Xiang Yao, Wu, Haohui, Wang, Wenjie, Feng, Fuli, Wang, Chao, Luan, Huanbo, Chua, Tat Seng
Large Vision-Language Models (LVLMs) have achieved remarkable performance in many vision-language tasks, yet their capabilities in fine-grained visual understanding remain insufficiently evaluated. Existing benchmarks either contain limited fine-grai
Externí odkaz:
http://arxiv.org/abs/2410.21311
Agents powered by large language models have shown remarkable reasoning and execution capabilities, attracting researchers to explore their potential in the recommendation domain. Previous studies have primarily focused on enhancing the capabilities
Externí odkaz:
http://arxiv.org/abs/2410.20027
Personalized content filtering, such as recommender systems, has become a critical infrastructure to alleviate information overload. However, these systems merely filter existing content and are constrained by its limited diversity, making it difficu
Externí odkaz:
http://arxiv.org/abs/2410.14170
Autor:
Lin, Xinyu, Yang, Chaoqun, Wang, Wenjie, Li, Yongqi, Du, Cunxiao, Feng, Fuli, Ng, See-Kiong, Chua, Tat-Seng
Large Language Model (LLM)-based generative recommendation has achieved notable success, yet its practical deployment is costly particularly due to excessive inference latency caused by autoregressive decoding. For lossless LLM decoding acceleration,
Externí odkaz:
http://arxiv.org/abs/2410.05165
Autor:
Wang, Taowen, Liu, Yiyang, Liang, James Chenhao, zhao, junhan, Cui, Yiming, Mao, Yuning, Nie, Shaoliang, Liu, Jiahao, Feng, Fuli, Xu, Zenglin, Han, Cheng, Huang, Lifu, Wang, Qifan, Liu, Dongfang
Multimodal Large Language Models (MLLMs) demonstrate remarkable performance across a wide range of domains, with increasing emphasis on enhancing their zero-shot generalization capabilities for unseen tasks across various modalities. Instruction tuni
Externí odkaz:
http://arxiv.org/abs/2409.15657
Recommending items solely catering to users' historical interests narrows users' horizons. Recent works have considered steering target users beyond their historical interests by directly adjusting items exposed to them. However, the recommended item
Externí odkaz:
http://arxiv.org/abs/2409.08934
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
Recommender systems have achieved increasing accuracy over the years. However, this precision often leads users to narrow their interests, resulting in issues such as limited diversity and the creation of echo chambers. Current research addresses the
Externí odkaz:
http://arxiv.org/abs/2409.04827