Zobrazeno 1 - 10
of 3 731
pro vyhledávání: '"Wang, Yuqing"'
Autor:
Wang, Yuqing, Fariha, Anna
CoWrangler is a data-wrangling recommender system designed to streamline data processing tasks. Recognizing that data processing is often time-consuming and complex for novice users, we aim to simplify the decision-making process regarding the most e
Externí odkaz:
http://arxiv.org/abs/2409.10635
Unifying Visual and Semantic Feature Spaces with Diffusion Models for Enhanced Cross-Modal Alignment
Image classification models often demonstrate unstable performance in real-world applications due to variations in image information, driven by differing visual perspectives of subject objects and lighting discrepancies. To mitigate these challenges,
Externí odkaz:
http://arxiv.org/abs/2407.18854
Non-contrast CT (NCCT) imaging may reduce image contrast and anatomical visibility, potentially increasing diagnostic uncertainty. In contrast, contrast-enhanced CT (CECT) facilitates the observation of regions of interest (ROI). Leading generative m
Externí odkaz:
http://arxiv.org/abs/2406.13977
Most existing theoretical investigations of the accuracy of diffusion models, albeit significant, assume the score function has been approximated to a certain accuracy, and then use this a priori bound to control the error of generation. This article
Externí odkaz:
http://arxiv.org/abs/2406.12839
Autor:
Xia, Haotian, Yang, Zhengbang, Zhao, Yun, Wang, Yuqing, Li, Jingxi, Tracy, Rhys, Zhu, Zhuangdi, Wang, Yuan-fang, Chen, Hanjie, Shen, Weining
Recent integration of Natural Language Processing (NLP) and multimodal models has advanced the field of sports analytics. This survey presents a comprehensive review of the datasets and applications driving these innovations post-2020. We overviewed
Externí odkaz:
http://arxiv.org/abs/2406.12252
Autor:
Wang, Yuqing, Zhao, Yun, Keller, Sara Alessandra, de Hond, Anne, van Buchem, Marieke M., Pillai, Malvika, Hernandez-Boussard, Tina
The advancement of large language models (LLMs) has demonstrated strong capabilities across various applications, including mental health analysis. However, existing studies have focused on predictive performance, leaving the critical issue of fairne
Externí odkaz:
http://arxiv.org/abs/2406.12033
Autor:
Wang, Yuqing, Zhao, Yun
With the increasing use of large language models (LLMs), ensuring reliable performance in diverse, real-world environments is essential. Despite their remarkable achievements, LLMs often struggle with adversarial inputs, significantly impacting their
Externí odkaz:
http://arxiv.org/abs/2406.11020
Autor:
Wang, Yuqing, Mäntylä, Mika V., Demeyer, Serge, Beyazit, Mutlu, Kisaakye, Joanna, Nyyssölä, Jesse
Microservice-based systems (MSS) may experience failures in various fault categories due to their complex and dynamic nature. To effectively handle failures, AIOps tools utilize trace-based anomaly detection and root cause analysis. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2403.18998
Geographical, physical, or economic constraints often result in missing traces within seismic data, making the reconstruction of complete seismic data a crucial step in seismic data processing. Traditional methods for seismic data reconstruction requ
Externí odkaz:
http://arxiv.org/abs/2403.11482
Autor:
Wang, Yuqing, Zhou, Yuan
Let $\Omega$ be a domain of $\mathbb R^n$ with $n\ge 2$ and $p(\cdot)$ be a local Lipschitz funcion in $\Omega$ with $1
Externí odkaz:
http://arxiv.org/abs/2403.03784