Zobrazeno 1 - 10
of 51 579
pro vyhledávání: '"zhang, Min"'
Autor:
Yu, Jiawei, Geng, Xiang, Li, Yuang, Ren, Mengxin, Tang, Wei, Li, Jiahuan, Lan, Zhibin, Zhang, Min, Yang, Hao, Huang, Shujian, Su, Jinsong
Spoken named entity recognition (NER) aims to identify named entities from speech, playing an important role in speech processing. New named entities appear every day, however, annotating their Spoken NER data is costly. In this paper, we demonstrate
Externí odkaz:
http://arxiv.org/abs/2412.19102
By using two-dimensional particle-in-cell simulations, attosecond electron bunches with high density, high energy and small divergence angle can be obtained by p-polarized laser irradiation in conical channel with curved wall. We find that some elect
Externí odkaz:
http://arxiv.org/abs/2412.18765
Autor:
Zhao, Xinping, Hu, Baotian, Zhong, Yan, Huang, Shouzheng, Zheng, Zihao, Wang, Meng, Wang, Haofen, zhang, Min
Although prevailing supervised and self-supervised learning (SSL)-augmented sequential recommendation (SeRec) models have achieved improved performance with powerful neural network architectures, we argue that they still suffer from two limitations:
Externí odkaz:
http://arxiv.org/abs/2412.18378
Autor:
Zhang, Xin, Zhang, Yanzhao, Xie, Wen, Li, Mingxin, Dai, Ziqi, Long, Dingkun, Xie, Pengjun, Zhang, Meishan, Li, Wenjie, Zhang, Min
Universal Multimodal Retrieval (UMR) aims to enable search across various modalities using a unified model, where queries and candidates can consist of pure text, images, or a combination of both. Previous work has attempted to adopt multimodal large
Externí odkaz:
http://arxiv.org/abs/2412.16855
Local Differential Privacy (LDP) is widely adopted in the Industrial Internet of Things (IIoT) for its lightweight, decentralized, and scalable nature. However, its perturbation-based privacy mechanism makes it difficult to distinguish between uncont
Externí odkaz:
http://arxiv.org/abs/2412.15704
Autor:
Zhou, Xiabin, Wang, Wenbin, Zeng, Minyan, Guo, Jiaxian, Liu, Xuebo, Shen, Li, Zhang, Min, Ding, Liang
Efficient KV cache management in LLMs is crucial for long-context tasks like RAG and summarization. Existing KV cache compression methods enforce a fixed pattern, neglecting task-specific characteristics and reducing the retention of essential inform
Externí odkaz:
http://arxiv.org/abs/2412.14838
Autor:
Lu, Yifan, Zhou, Yigeng, Li, Jing, Wang, Yequan, Liu, Xuebo, He, Daojing, Liu, Fangming, Zhang, Min
Multi-hop question answering (MHQA) poses a significant challenge for large language models (LLMs) due to the extensive knowledge demands involved. Knowledge editing, which aims to precisely modify the LLMs to incorporate specific knowledge without n
Externí odkaz:
http://arxiv.org/abs/2412.13782
Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across diverse tasks. Despite great success, recent studies show that LVLMs encounter substantial limitations when engaging with visual graphs. To study the reason behind t
Externí odkaz:
http://arxiv.org/abs/2412.13540
Large language models (LLMs) based on generative pre-trained Transformer have achieved remarkable performance on knowledge graph question-answering (KGQA) tasks. However, LLMs often produce ungrounded subgraph planning or reasoning results in KGQA du
Externí odkaz:
http://arxiv.org/abs/2412.12643
Visual information has been introduced for enhancing machine translation (MT), and its effectiveness heavily relies on the availability of large amounts of bilingual parallel sentence pairs with manual image annotations. In this paper, we introduce a
Externí odkaz:
http://arxiv.org/abs/2412.12627