Zobrazeno 1 - 10
of 694
pro vyhledávání: '"Guo, Yuhong"'
Autor:
Xiong, Yi, Wu, Hao, Shao, Changxu, Wang, Ziqing, Zhang, Rui, Guo, Yuhong, Zhao, Junping, Zhang, Ke, Pan, Zhenxuan
The expanding context windows in large language models (LLMs) have greatly enhanced their capabilities in various applications, but they also introduce significant challenges in maintaining low latency, particularly in Time to First Token (TTFT). Thi
Externí odkaz:
http://arxiv.org/abs/2410.00428
Autor:
Xu, Jiale, Zhang, Rui, Guo, Cong, Hu, Weiming, Liu, Zihan, Wu, Feiyang, Feng, Yu, Sun, Shixuan, Shao, Changxu, Guo, Yuhong, Zhao, Junping, Zhang, Ke, Guo, Minyi, Leng, Jingwen
Large Language Models (LLMs) are widely used across various domains, processing millions of daily requests. This surge in demand poses significant challenges in optimizing throughput and latency while keeping costs manageable. The Key-Value (KV) cach
Externí odkaz:
http://arxiv.org/abs/2407.15309
In recent years, semi-supervised learning (SSL) has gained significant attention due to its ability to leverage both labeled and unlabeled data to improve model performance, especially when labeled data is scarce. However, most current SSL methods re
Externí odkaz:
http://arxiv.org/abs/2405.01760
Autor:
En, Qing, Guo, Yuhong
Medical image segmentation typically demands extensive dense annotations for model training, which is both time-consuming and skill-intensive. To mitigate this burden, exemplar-based medical image segmentation methods have been introduced to achieve
Externí odkaz:
http://arxiv.org/abs/2404.11812
In this paper, we present a novel approach termed Prompt-Driven Feature Diffusion (PDFD) within a semi-supervised learning framework for Open World Semi-Supervised Learning (OW-SSL). At its core, PDFD deploys an efficient feature-level diffusion mode
Externí odkaz:
http://arxiv.org/abs/2404.11795
Autor:
Yan, Hao, Guo, Yuhong
Federated learning aims to tackle the ``isolated data island" problem, where it trains a collective model from physically isolated clients while safeguarding the privacy of users' data. However, supervised federated learning necessitates that each cl
Externí odkaz:
http://arxiv.org/abs/2404.11046
Autor:
En, Qing, Guo, Yuhong
Lung-infected area segmentation is crucial for assessing the severity of lung diseases. However, existing image-text multi-modal methods typically rely on labour-intensive annotations for model training, posing challenges regarding time and expertise
Externí odkaz:
http://arxiv.org/abs/2404.11008
Due to the availability of only a few labeled instances for the novel target prediction task and the significant domain shift between the well annotated source domain and the target domain, cross-domain few-shot learning (CDFSL) induces a very challe
Externí odkaz:
http://arxiv.org/abs/2312.03928
Graph Neural Networks (GNNs) have been shown to possess strong representation abilities over graph data. However, GNNs are vulnerable to adversarial attacks, and even minor perturbations to the graph structure can significantly degrade their performa
Externí odkaz:
http://arxiv.org/abs/2309.10136
Autor:
Alchihabi, Abdullah, Guo, Yuhong
Graph Neural Networks (GNNs) require a large number of labeled graph samples to obtain good performance on the graph classification task. The performance of GNNs degrades significantly as the number of labeled graph samples decreases. To reduce the a
Externí odkaz:
http://arxiv.org/abs/2309.10134