Zobrazeno 1 - 10
of 505
pro vyhledávání: '"Yamana, Hayato"'
Autor:
Zhang, Yuxiang, Chen, Jing, Wang, Junjie, Liu, Yaxin, Yang, Cheng, Shi, Chufan, Zhu, Xinyu, Lin, Zihao, Wan, Hanwen, Yang, Yujiu, Sakai, Tetsuya, Feng, Tian, Yamana, Hayato
Tool-augmented large language models (LLMs) are rapidly being integrated into real-world applications. Due to the lack of benchmarks, the community still needs to fully understand the hallucination issues within these models. To address this challeng
Externí odkaz:
http://arxiv.org/abs/2406.20015
Autor:
Yada, Yuki, Yamana, Hayato
Personalized news recommendations are essential for online news platforms to assist users in discovering news articles that match their interests from a vast amount of online content. Appropriately encoded content features, such as text, categories,
Externí odkaz:
http://arxiv.org/abs/2405.13007
Dark patterns are deceptive user interface designs for online services that make users behave in unintended ways. Dark patterns, such as privacy invasion, financial loss, and emotional distress, can harm users. These issues have been the subject of c
Externí odkaz:
http://arxiv.org/abs/2401.04119
Named-entity recognition (NER) detects texts with predefined semantic labels and is an essential building block for natural language processing (NLP). Notably, recent NER research focuses on utilizing massive extra data, including pre-training corpor
Externí odkaz:
http://arxiv.org/abs/2305.03970
Dark patterns, which are user interface designs in online services, induce users to take unintended actions. Recently, dark patterns have been raised as an issue of privacy and fairness. Thus, a wide range of research on detecting dark patterns is ea
Externí odkaz:
http://arxiv.org/abs/2211.06543
Autor:
Ono, Sachiko, Sasabuchi, Yusuke, Yamana, Hayato, Yokota, Isao, Okada, Akira, Matsui, Hiroki, Itai, Shunsuke, Yonenaga, Kazumichi, Tonosaki, Kanata, Watanabe, Rinji, Ono, Yosuke, Yasunaga, Hideo, Hoshi, Kazuto
Publikováno v:
In Archives of Gerontology and Geriatrics May 2024 120
Autor:
Watanabe, Satoru, Yamana, Hayato
The inner representation of deep neural networks (DNNs) is indecipherable, which makes it difficult to tune DNN models, control their training process, and interpret their outputs. In this paper, we propose a novel approach to investigate the inner r
Externí odkaz:
http://arxiv.org/abs/2106.03016
Publikováno v:
In Neurocomputing 28 February 2024 571
In the big data era, cloud-based machine learning as a service (MLaaS) has attracted considerable attention. However, when handling sensitive data, such as financial and medical data, a privacy issue emerges, because the cloud server can access clien
Externí odkaz:
http://arxiv.org/abs/2009.03727
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