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pro vyhledávání: '"Vo, Nguyen"'
Autor:
Hwang, EunJeong, Zhou, Yichao, Wendt, James Bradley, Gunel, Beliz, Vo, Nguyen, Xie, Jing, Tata, Sandeep
Large language models (LLMs) often struggle with processing extensive input contexts, which can lead to redundant, inaccurate, or incoherent summaries. Recent methods have used unstructured memory to incrementally process these contexts, but they sti
Externí odkaz:
http://arxiv.org/abs/2407.15021
Next-generation wireless networks are projected to empower a broad range of Internet-of-things (IoT) applications and services with extreme data rates, posing new challenges in delivering large-scale connectivity at a low cost to current communicatio
Externí odkaz:
http://arxiv.org/abs/2406.01921
Autor:
Gunel, Beliz, Wendt, James B., Xie, Jing, Zhou, Yichao, Vo, Nguyen, Fisher, Zachary, Tata, Sandeep
Users often struggle with decision-making between two options (A vs B), as it usually requires time-consuming research across multiple web pages. We propose STRUM-LLM that addresses this challenge by generating attributed, structured, and helpful con
Externí odkaz:
http://arxiv.org/abs/2403.19710
AI technology for generating images, such as diffusion models, has advanced rapidly. However, there is no established framework for quantifying the reliability of AI-generated images, which hinders their use in critical decision-making tasks, such as
Externí odkaz:
http://arxiv.org/abs/2402.11789
In this study, we consider the reliability assessment of anomaly detection (AD) using Variational Autoencoder (VAE). Over the last decade, VAE-based AD has been actively studied in various perspective, from method development to applied research. How
Externí odkaz:
http://arxiv.org/abs/2402.03724
Autor:
Shiraishi, Tomohiro, Miwa, Daiki, Katsuoka, Teruyuki, Duy, Vo Nguyen Le, Taji, Kouichi, Takeuchi, Ichiro
The Vision Transformer (ViT) demonstrates exceptional performance in various computer vision tasks. Attention is crucial for ViT to capture complex wide-ranging relationships among image patches, allowing the model to weigh the importance of image pa
Externí odkaz:
http://arxiv.org/abs/2401.08169
We propose a novel statistical method for testing the results of anomaly detection (AD) under domain adaptation (DA), which we call CAD-DA -- controllable AD under DA. The distinct advantage of the CAD-DA lies in its ability to control the probabilit
Externí odkaz:
http://arxiv.org/abs/2310.14608