Zobrazeno 1 - 10
of 6 672
pro vyhledávání: '"Yang,Qiang"'
Autor:
Jin, Yilun, Li, Zheng, Zhang, Chenwei, Cao, Tianyu, Gao, Yifan, Jayarao, Pratik, Li, Mao, Liu, Xin, Sarkhel, Ritesh, Tang, Xianfeng, Wang, Haodong, Wang, Zhengyang, Xu, Wenju, Yang, Jingfeng, Yin, Qingyu, Li, Xian, Nigam, Priyanka, Xu, Yi, Chen, Kai, Yang, Qiang, Jiang, Meng, Yin, Bing
Online shopping is a complex multi-task, few-shot learning problem with a wide and evolving range of entities, relations, and tasks. However, existing models and benchmarks are commonly tailored to specific tasks, falling short of capturing the full
Externí odkaz:
http://arxiv.org/abs/2410.20745
Autor:
Yang, Qiang, Xie, Weilin, Wang, Congfan, Li, Bowen, Li, Xin, Zheng, Xiang, Wei, Wei, Dong, Yi
In distributed fiber-optic sensing based on optical frequency domain reflectometry (OFDR), Doppler frequency shifts due to the changes of disturbances during one sweep period introduce demodulation errors that accumulate along both the distance and t
Externí odkaz:
http://arxiv.org/abs/2410.19368
Federated Learning (FL) facilitates collaborative training of a global model whose performance is boosted by private data owned by distributed clients, without compromising data privacy. Yet the wide applicability of FL is hindered by entanglement of
Externí odkaz:
http://arxiv.org/abs/2410.12530
As large language models (LLMs) become increasingly prevalent in web services, effectively leveraging domain-specific knowledge while ensuring privacy has become critical. Existing methods, such as retrieval-augmented generation (RAG) and differentia
Externí odkaz:
http://arxiv.org/abs/2410.10481
Data and model heterogeneity are two core issues in Heterogeneous Federated Learning (HtFL). In scenarios with heterogeneous model architectures, aggregating model parameters becomes infeasible, leading to the use of prototypes (i.e., class represent
Externí odkaz:
http://arxiv.org/abs/2410.06490
Session-based Social Recommendation (SSR) leverages social relationships within online networks to enhance the performance of Session-based Recommendation (SR). However, existing SSR algorithms often encounter the challenge of "friend data sparsity".
Externí odkaz:
http://arxiv.org/abs/2409.02702
Autor:
Liang, Jinglin, Zhong, Jin, Gu, Hanlin, Lu, Zhongqi, Tang, Xingxing, Dai, Gang, Huang, Shuangping, Fan, Lixin, Yang, Qiang
Federated Class Continual Learning (FCCL) merges the challenges of distributed client learning with the need for seamless adaptation to new classes without forgetting old ones. The key challenge in FCCL is catastrophic forgetting, an issue that has b
Externí odkaz:
http://arxiv.org/abs/2409.01128
The automatic classification of animal sounds presents an enduring challenge in bioacoustics, owing to the diverse statistical properties of sound signals, variations in recording equipment, and prevalent low Signal-to-Noise Ratio (SNR) conditions. D
Externí odkaz:
http://arxiv.org/abs/2407.03440
The scaling law, which involves the brute-force expansion of training datasets and learnable parameters, has become a prevalent strategy for developing more robust learning models. However, due to bottlenecks in data, computation, and trust, the sust
Externí odkaz:
http://arxiv.org/abs/2407.00478
Autor:
Fan, Tao, Kang, Yan, Chen, Weijing, Gu, Hanlin, Song, Yuanfeng, Fan, Lixin, Chen, Kai, Yang, Qiang
In the context of real-world applications, leveraging large language models (LLMs) for domain-specific tasks often faces two major challenges: domain-specific knowledge privacy and constrained resources. To address these issues, we propose PDSS, a pr
Externí odkaz:
http://arxiv.org/abs/2406.12403