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pro vyhledávání: '"Li, Zilong"'
Autor:
Li, Zilong
This papers presents the submission of team Ryu to the canceled SIGMORPHON 2024 shared task on subword tokenization. My submission explores whether morphological segmentation methods can be used as a part of subword tokenizers. I adopt two approaches
Externí odkaz:
http://arxiv.org/abs/2410.17094
The language control network is vital among language-related networks responsible for solving the problem of multiple language switching. Researchers have expressed concerns about the instability of the language control network when exposed to extern
Externí odkaz:
http://arxiv.org/abs/2401.12616
Incomplete-view computed tomography (CT) can shorten the data acquisition time and allow scanning of large objects, including sparse-view and limited-angle scenarios, each with various settings, such as different view numbers or angular ranges. Howev
Externí odkaz:
http://arxiv.org/abs/2312.07846
Image restoration, which aims to retrieve and enhance degraded images, is fundamental across a wide range of applications. While conventional deep learning approaches have notably improved the image quality across various tasks, they still suffer fro
Externí odkaz:
http://arxiv.org/abs/2312.05038
Interpreting the decisions of deep learning models has been actively studied since the explosion of deep neural networks. One of the most convincing interpretation approaches is salience-based visual interpretation, such as Grad-CAM, where the genera
Externí odkaz:
http://arxiv.org/abs/2310.09821
Sparse-view computed tomography (CT) -- using a small number of projections for tomographic reconstruction -- enables much lower radiation dose to patients and accelerated data acquisition. The reconstructed images, however, suffer from strong artifa
Externí odkaz:
http://arxiv.org/abs/2308.08463
Publikováno v:
MICCAI 2023
Sparse-view computed tomography (CT) is a promising solution for expediting the scanning process and mitigating radiation exposure to patients, the reconstructed images, however, contain severe streak artifacts, compromising subsequent screening and
Externí odkaz:
http://arxiv.org/abs/2307.05890
Publikováno v:
MICCAI 2023
Lung nodule malignancy prediction has been enhanced by advanced deep-learning techniques and effective tricks. Nevertheless, current methods are mainly trained with cross-entropy loss using one-hot categorical labels, which results in difficulty in d
Externí odkaz:
http://arxiv.org/abs/2304.08013
Publikováno v:
IEEE Transactions on Medical Imaging, 43(2), 2024
Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon starvation and electronic noise. Recently, some works have attempted to use diffusion models to address the over-smoothness and training instability encountered by
Externí odkaz:
http://arxiv.org/abs/2304.01814
Publikováno v:
Humanities & Social Sciences Communications, Vol 11, Iss 1, Pp 1-17 (2024)
Abstract Institutions are pivotal in university governance, symbolizing stable organizational power reflective of governance capacity. The strategic organization of a university’s internal structures aims to align with its developmental goals. The
Externí odkaz:
https://doaj.org/article/e212bcc9cd6944f5812695f961a481df
