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Akademický článek

Assessment of invasion of prostate cancer with multiparametric MRI-based radiomics

Autor: YANG Jing, HUANG Doudou, CHEN Junfan

Publikováno v: 陆军军医大学学报, Vol 46, Iss 2, Pp 170-180 (2024)

Objective To investigate the value of a radiomic model of multiparametric MRI different regions of interest (ROI) in assessment of the invasion of prostate cancer (PCa), and explore the evaluation value of an integrated model based on above radiomic

Externí odkaz: https://doaj.org/article/433cfcf14c564161b10c082e13860d9d

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HOLA-Drone: Hypergraphic Open-ended Learning for Zero-Shot Multi-Drone Cooperative Pursuit

Autor: Li, Yang, Zhang, Dengyu, Chen, Junfan, Wen, Ying, Zhang, Qingrui, Mou, Shaoshuai, Pan, Wei

Zero-shot coordination (ZSC) is a significant challenge in multi-agent collaboration, aiming to develop agents that can coordinate with unseen partners they have not encountered before. Recent cutting-edge ZSC methods have primarily focused on two-pl

Externí odkaz: http://arxiv.org/abs/2409.08767

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Progressively Modality Freezing for Multi-Modal Entity Alignment

Autor: Huang, Yani, Zhang, Xuefeng, Zhang, Richong, Chen, Junfan, Kim, Jaein

Multi-Modal Entity Alignment aims to discover identical entities across heterogeneous knowledge graphs. While recent studies have delved into fusion paradigms to represent entities holistically, the elimination of features irrelevant to alignment and

Externí odkaz: http://arxiv.org/abs/2407.16168

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Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching

Autor: Li, Zhuoran, Hu, Chunming, Chen, Junfan, Chen, Zhijun, Guo, Xiaohui, Zhang, Richong

Code-switching is a data augmentation scheme mixing words from multiple languages into source lingual text. It has achieved considerable generalization performance of cross-lingual transfer tasks by aligning cross-lingual contextual word representati

Externí odkaz: http://arxiv.org/abs/2406.13361

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Adversarial Word Dilution as Text Data Augmentation in Low-Resource Regime

Autor: Chen, Junfan, Zhang, Richong, Luo, Zheyan, Hu, Chunming, Mao, Yongyi

Data augmentation is widely used in text classification, especially in the low-resource regime where a few examples for each class are available during training. Despite the success, generating data augmentations as hard positive examples that may in

Externí odkaz: http://arxiv.org/abs/2305.09287

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ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification

Autor: Chen, Junfan, Zhang, Richong, Mao, Yongyi, Xu, Jie

Few-shot text classification has recently been promoted by the meta-learning paradigm which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes. Despite their success, existing works

Externí odkaz: http://arxiv.org/abs/2305.09269

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A Hierarchical N-Gram Framework for Zero-Shot Link Prediction

Autor: Li, Mingchen, Chen, Junfan, Mensah, Samuel, Aletras, Nikolaos, Yang, Xiulong, Ye, Yang

Due to the incompleteness of knowledge graphs (KGs), zero-shot link prediction (ZSLP) which aims to predict unobserved relations in KGs has attracted recent interest from researchers. A common solution is to use textual features of relations (e.g., s

Externí odkaz: http://arxiv.org/abs/2204.10293

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