Zobrazeno 1 - 10
of 18 355
pro vyhledávání: '"MON, A."'
Autor:
Feng, Yu, Htut, Phu Mon, Qi, Zheng, Xiao, Wei, Mager, Manuel, Pappas, Nikolaos, Halder, Kishaloy, Li, Yang, Benajiba, Yassine, Roth, Dan
Quantifying the uncertainty in the factual parametric knowledge of Large Language Models (LLMs), especially in a black-box setting, poses a significant challenge. Existing methods, which gauge a model's uncertainty through evaluating self-consistency
Externí odkaz:
http://arxiv.org/abs/2412.09572
Autor:
Liu, Siyi, Ning, Qiang, Halder, Kishaloy, Xiao, Wei, Qi, Zheng, Htut, Phu Mon, Zhang, Yi, John, Neha Anna, Min, Bonan, Benajiba, Yassine, Roth, Dan
Open domain question answering systems frequently rely on information retrieved from large collections of text (such as the Web) to answer questions. However, such collections of text often contain conflicting information, and indiscriminately depend
Externí odkaz:
http://arxiv.org/abs/2410.12311
Autor:
Li, Gen, Tsagkas, Nikolaos, Song, Jifei, Mon-Williams, Ruaridh, Vijayakumar, Sethu, Shao, Kun, Sevilla-Lara, Laura
Affordance, defined as the potential actions that an object offers, is crucial for robotic manipulation tasks. A deep understanding of affordance can lead to more intelligent AI systems. For example, such knowledge directs an agent to grasp a knife b
Externí odkaz:
http://arxiv.org/abs/2408.10123
Autor:
Gysel, Philipp, Wüest, Candid, Nwafor, Kenneth, Jašek, Otakar, Ustyuzhanin, Andrey, Divakaran, Dinil Mon
Securing endpoints is challenging due to the evolving nature of threats and attacks. With endpoint logging systems becoming mature, provenance-graph representations enable the creation of sophisticated behavior rules. However, adapting to the pace of
Externí odkaz:
http://arxiv.org/abs/2408.09217
To address the challenging problem of detecting phishing webpages, researchers have developed numerous solutions, in particular those based on machine learning (ML) algorithms. Among these, brand-based phishing detection that uses models from Compute
Externí odkaz:
http://arxiv.org/abs/2408.05941
Phishing attacks attempt to deceive users into stealing sensitive information, posing a significant cybersecurity threat. Advances in machine learning (ML) and deep learning (DL) have led to the development of numerous phishing webpage detection solu
Externí odkaz:
http://arxiv.org/abs/2407.20361
Completing complex tasks in unpredictable settings like home kitchens challenges robotic systems. These challenges include interpreting high-level human commands, such as "make me a hot beverage" and performing actions like pouring a precise amount o
Externí odkaz:
http://arxiv.org/abs/2406.11231
Autor:
Chaya, Veasna, Poortinga, Ate, Nimol, Keo, Sokleap, Se, Sophorn, Mon, Chhin, Phy, McMahon, Andrea, Nicolau, Andrea Puzzi, Tenneson, Karis, Saah, David
Cambodia's agricultural landscape is rapidly transforming, particularly in the cashew sector. Despite the country's rapid emergence and ambition to become the largest cashew producer, comprehensive data on plantation areas and the environmental impac
Externí odkaz:
http://arxiv.org/abs/2405.16926
Large language models (LLMs) are a class of powerful and versatile models that are beneficial to many industries. With the emergence of LLMs, we take a fresh look at cyber security, specifically exploring and summarizing the potential of LLMs in addr
Externí odkaz:
http://arxiv.org/abs/2404.11338
Quantification, also known as class prevalence estimation, is the supervised learning task in which a model is trained to predict the prevalence of each class in a given bag of examples. This paper investigates the application of deep neural networks
Externí odkaz:
http://arxiv.org/abs/2403.15123