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pro vyhledávání: '"Wu, Yifan"'
Parser-based log compressors have been widely explored in recent years because the explosive growth of log volumes makes the compression performance of general-purpose compressors unsatisfactory. These parser-based compressors preprocess logs by grou
Externí odkaz:
http://arxiv.org/abs/2408.05760
Previous studies aiming to optimize and bundle-adjust camera poses using Neural Radiance Fields (NeRFs), such as BARF and DBARF, have demonstrated impressive capabilities in 3D scene reconstruction. However, these approaches have been designed for pi
Externí odkaz:
http://arxiv.org/abs/2408.01878
In this study, we introduce the More-Interaction Particle Transformer (MIParT), a novel deep learning neural network designed for jet tagging. This framework incorporates our own design, the More-Interaction Attention (MIA) mechanism, which increases
Externí odkaz:
http://arxiv.org/abs/2407.08682
Autor:
Zhang, Lingzhe, Jia, Tong, Jia, Mengxi, Wu, Yifan, Liu, Aiwei, Yang, Yong, Wu, Zhonghai, Hu, Xuming, Yu, Philip S., Li, Ying
As software systems grow increasingly intricate, Artificial Intelligence for IT Operations (AIOps) methods have been widely used in software system failure management to ensure the high availability and reliability of large-scale distributed software
Externí odkaz:
http://arxiv.org/abs/2406.11213
Autor:
Wu, Yifan, Hartline, Jason
Scoring rules evaluate probabilistic forecasts of an unknown state against the realized state and are a fundamental building block in the incentivized elicitation of information and the training of machine learning models. This paper develops mechani
Externí odkaz:
http://arxiv.org/abs/2406.09363
Log parsing transforms log messages into structured formats, serving as a crucial step for log analysis. Despite a variety of log parsing methods that have been proposed, their performance on evolving log data remains unsatisfactory due to reliance o
Externí odkaz:
http://arxiv.org/abs/2406.03376
Autor:
Nanayakkara, Priyanka, Kim, Hyeok, Wu, Yifan, Sarvghad, Ali, Mahyar, Narges, Miklau, Gerome, Hullman, Jessica
Publikováno v:
in 2024 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, 2024 pp. 231-231
Differential privacy (DP) has the potential to enable privacy-preserving analysis on sensitive data, but requires analysts to judiciously spend a limited ``privacy loss budget'' $\epsilon$ across queries. Analysts conducting exploratory analyses do n
Externí odkaz:
http://arxiv.org/abs/2406.01964
Autor:
Yang, Yue, Gandhi, Mona, Wang, Yufei, Wu, Yifan, Yao, Michael S., Callison-Burch, Chris, Gee, James C., Yatskar, Mark
While deep networks have achieved broad success in analyzing natural images, when applied to medical scans, they often fail in unexcepted situations. We investigate this challenge and focus on model sensitivity to domain shifts, such as data sampled
Externí odkaz:
http://arxiv.org/abs/2405.14839
In this paper, we explore a forward-thinking question: Is GPT-4V effective at low-level data analysis tasks on charts? To this end, we first curate a large-scale dataset, named ChartInsights, consisting of 89,388 quartets (chart, task, question, answ
Externí odkaz:
http://arxiv.org/abs/2405.07001
Autor:
Hu, Lunjia, Wu, Yifan
Calibration allows predictions to be reliably interpreted as probabilities by decision makers. We propose a decision-theoretic calibration error, the Calibration Decision Loss (CDL), defined as the maximum improvement in decision payoff obtained by c
Externí odkaz:
http://arxiv.org/abs/2404.13503