Zobrazeno 1 - 10
of 56 666
pro vyhledávání: '"Kim, Jin A"'
Large language models (LLMs) have demonstrated strong capabilities across various language tasks, notably through instruction-tuning methods. However, LLMs face challenges in visualizing complex, real-world data through charts and plots. Firstly, exi
Externí odkaz:
http://arxiv.org/abs/2410.04064
Autor:
Oh, Yujin, Park, Sangjoon, Li, Xiang, Yi, Wang, Paly, Jonathan, Efstathiou, Jason, Chan, Annie, Kim, Jun Won, Byun, Hwa Kyung, Lee, Ik Jae, Cho, Jaeho, Wee, Chan Woo, Shu, Peng, Wang, Peilong, Yu, Nathan, Holmes, Jason, Ye, Jong Chul, Li, Quanzheng, Liu, Wei, Koom, Woong Sub, Kim, Jin Sung, Kim, Kyungsang
Clinical experts employ diverse philosophies and strategies in patient care, influenced by regional patient populations. However, existing medical artificial intelligence (AI) models are often trained on data distributions that disproportionately ref
Externí odkaz:
http://arxiv.org/abs/2410.00046
Beta-amyloid positron emission tomography (A$\beta$-PET) imaging has become a critical tool in Alzheimer's disease (AD) research and diagnosis, providing insights into the pathological accumulation of amyloid plaques, one of the hallmarks of AD. Howe
Externí odkaz:
http://arxiv.org/abs/2409.18282
ESPERANTO: Evaluating Synthesized Phrases to Enhance Robustness in AI Detection for Text Origination
Autor:
Ayoobi, Navid, Knab, Lily, Cheng, Wen, Pantoja, David, Alikhani, Hamidreza, Flamant, Sylvain, Kim, Jin, Mukherjee, Arjun
While large language models (LLMs) exhibit significant utility across various domains, they simultaneously are susceptible to exploitation for unethical purposes, including academic misconduct and dissemination of misinformation. Consequently, AI-gen
Externí odkaz:
http://arxiv.org/abs/2409.14285
In radiation therapy (RT), the reliance on pre-treatment computed tomography (CT) images encounter challenges due to anatomical changes, necessitating adaptive planning. Daily cone-beam CT (CBCT) imaging, pivotal for therapy adjustment, falls short i
Externí odkaz:
http://arxiv.org/abs/2409.12539
Recent advancements in automatic speaker verification (ASV) studies have been achieved by leveraging large-scale pretrained networks. In this study, we analyze the approaches toward such a paradigm and underline the significance of interlayer informa
Externí odkaz:
http://arxiv.org/abs/2409.07770
Autor:
Rödel, Maximilian, Philipp, Luca Nils, Kim, Jin Hong, Lehmann, Matthias, Stolte, Matthias, Mitric, Roland, Würthner, Frank, Pflaum, Jens
Exciton plasmon polaritons have gained increasing interests over recent years due to their versatile properties emerging by the underlying light-matter coupling and making them potential candidates for new photonic applications. We have advanced this
Externí odkaz:
http://arxiv.org/abs/2409.01210
A high-brightness entangled photon pair (HBEPP) source is essential for conducting entanglement-based quantum key distribution (QKD) between a satellite and a ground station. While an ultrabright source can overcome significant losses in satellite-ba
Externí odkaz:
http://arxiv.org/abs/2408.14768
Autor:
Lee, Hyeongmin, Kim, Jin-Young, Baek, Kyungjune, Kim, Jihwan, Go, Hyojun, Ha, Seongsu, Han, Seokjin, Jang, Jiho, Jung, Raehyuk, Kim, Daewoo, Kim, GeunOh, Kim, JongMok, Kim, Jongseok, Kim, Junwan, Kwon, Soonwoo, Lee, Jangwon, Park, Seungjoon, Seo, Minjoon, Suh, Jay, Yi, Jaehyuk, Lee, Aiden
In this work, we discuss evaluating video foundation models in a fair and robust manner. Unlike language or image foundation models, many video foundation models are evaluated with differing parameters (such as sampling rate, number of frames, pretra
Externí odkaz:
http://arxiv.org/abs/2408.11318
Enhancing Source-Free Domain Adaptive Object Detection with Low-confidence Pseudo Label Distillation
Source-Free domain adaptive Object Detection (SFOD) is a promising strategy for deploying trained detectors to new, unlabeled domains without accessing source data, addressing significant concerns around data privacy and efficiency. Most SFOD methods
Externí odkaz:
http://arxiv.org/abs/2407.13524