Zobrazeno 1 - 10
of 170 629
pro vyhledávání: '"Anwar, ."'
Nowadays, the rapid diffusion of fake news poses a significant problem, as it can spread misinformation and confusion. This paper aims to develop an advanced machine learning solution for detecting fake news articles. Leveraging a comprehensive datas
Externí odkaz:
http://arxiv.org/abs/2411.10713
Autor:
Nejad, Sareh Soltani, Haque, Anwar
The widespread implementation of urban surveillance systems has necessitated more sophisticated techniques for anomaly detection to ensure enhanced public safety. This paper presents a significant advancement in the field of anomaly detection through
Externí odkaz:
http://arxiv.org/abs/2411.08755
A key objective of interpretability research on large language models (LLMs) is to develop methods for robustly steering models toward desired behaviors. To this end, two distinct approaches to interpretability -- ``bottom-up" and ``top-down" -- have
Externí odkaz:
http://arxiv.org/abs/2411.07213
Advancements in tracking algorithms have empowered nascent applications across various domains, from steering autonomous vehicles to guiding robots to enhancing augmented reality experiences for users. However, these algorithms are application-specif
Externí odkaz:
http://arxiv.org/abs/2411.07146
Software updates are essential to enhance security, fix bugs, and add better features to existing software. However, while some users comply and update their systems upon notification, non-compliance is common. Delaying or ignoring updates leaves sys
Externí odkaz:
http://arxiv.org/abs/2411.06262
Enhancing presence in mixed reality (MR) relies on precise measurement and quantification. While presence has traditionally been measured through subjective questionnaires, recent research links presence with objective metrics like reaction time. Pas
Externí odkaz:
http://arxiv.org/abs/2411.05272
Distractions in mixed reality (MR) environments can significantly influence user experience, affecting key factors such as presence, reaction time, cognitive load, and Break in Presence (BIP). Presence measures immersion, reaction time captures user
Externí odkaz:
http://arxiv.org/abs/2411.05275
Understanding group behavior is essential for improving collaboration and productivity. While research on group behavior in virtual reality (VR) is significantly advanced, understanding group dynamics in mixed reality (MR) remains understudied. Under
Externí odkaz:
http://arxiv.org/abs/2411.05258
Transformers have demonstrated remarkable in-context learning capabilities across various domains, including statistical learning tasks. While previous work has shown that transformers can implement common learning algorithms, the adversarial robustn
Externí odkaz:
http://arxiv.org/abs/2411.05189
Zero-shot coordination (ZSC) is a popular setting for studying the ability of reinforcement learning (RL) agents to coordinate with novel partners. Prior ZSC formulations assume the $\textit{problem setting}$ is common knowledge: each agent knows the
Externí odkaz:
http://arxiv.org/abs/2411.04976