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pro vyhledávání: '"KIM, Sung"'
The increasing adoption of AI-generated radiology reports necessitates robust methods for detecting hallucinations--false or unfounded statements that could impact patient care. We present ReXTrust, a novel framework for fine-grained hallucination de
Externí odkaz:
http://arxiv.org/abs/2412.15264
Medical vision-language model models often struggle with generating accurate quantitative measurements in radiology reports, leading to hallucinations that undermine clinical reliability. We introduce FactCheXcker, a modular framework that de-halluci
Externí odkaz:
http://arxiv.org/abs/2411.18672
Autor:
Ginting, Muhammad Fadhil, Kim, Dong-Ki, Kim, Sung-Kyun, Krishna, Bandi Jai, Kochenderfer, Mykel J., Omidshafiei, Shayegan, Agha-mohammadi, Ali-akbar
This paper addresses the problem of task planning for robots that must comply with operational manuals in real-world settings. Task planning under these constraints is essential for enabling autonomous robot operation in domains that require adherenc
Externí odkaz:
http://arxiv.org/abs/2411.11323
Learning generalized models from biased data is an important undertaking toward fairness in deep learning. To address this issue, recent studies attempt to identify and leverage bias-conflicting samples free from spurious correlations without prior k
Externí odkaz:
http://arxiv.org/abs/2411.00360
Electroencephalography (EEG) is a generally used neuroimaging approach in brain-computer interfaces due to its non-invasive characteristics and convenience, making it an effective tool for understanding human intentions. Therefore, recent research ha
Externí odkaz:
http://arxiv.org/abs/2411.10450
Brain-computer interface (BCI) enables direct communication between the brain and external devices by decoding neural signals, offering potential solutions for individuals with motor impairments. This study explores the neural signatures of motor exe
Externí odkaz:
http://arxiv.org/abs/2411.05811
The detection of pilots' mental states is critical, as abnormal mental states have the potential to cause catastrophic accidents. This study demonstrates the feasibility of using deep learning techniques to classify different fatigue levels, specific
Externí odkaz:
http://arxiv.org/abs/2411.09707
We propose a framework for topological quantum computation using newly discovered non-semisimple analogs of topological quantum field theories in 2+1 dimensions. These enhanced theories offer more powerful models for quantum computation. The conventi
Externí odkaz:
http://arxiv.org/abs/2410.14860
Autor:
Becker, Ruben, Cenzato, Davide, Kim, Sung-Hwan, Kociumaka, Tomasz, Kodric, Bojana, Policriti, Alberto, Prezza, Nicola
Co-lex partial orders were recently introduced in (Cotumaccio et al., SODA 2021 and JACM 2023) as a powerful tool to index finite state automata, with applications to regular expression matching. They generalize Wheeler orders (Gagie et al., Theoreti
Externí odkaz:
http://arxiv.org/abs/2410.04771
Autor:
Jang, Doohyuk, Park, Sihwan, Yang, June Yong, Jung, Yeonsung, Yun, Jihun, Kundu, Souvik, Kim, Sung-Yub, Yang, Eunho
Auto-Regressive (AR) models have recently gained prominence in image generation, often matching or even surpassing the performance of diffusion models. However, one major limitation of AR models is their sequential nature, which processes tokens one
Externí odkaz:
http://arxiv.org/abs/2410.03355