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of 1 402
pro vyhledávání: '"Vo, Nguyen"'
Detecting the presence of anomalies in regression models is a crucial task in machine learning, as anomalies can significantly impact the accuracy and reliability of predictions. Random Sample Consensus (RANSAC) is one of the most popular robust regr
Externí odkaz:
http://arxiv.org/abs/2410.15133
Feature Selection (FS) under domain adaptation (DA) is a critical task in machine learning, especially when dealing with limited target data. However, existing methods lack the capability to guarantee the reliability of FS under DA. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2410.15022
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
Advancements in AI image generation, particularly diffusion models, have progressed rapidly. However, the absence of an established framework for quantifying the reliability of AI-generated images hinders their use in critical decision-making tasks,
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