Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Yuan, Nicholas Jing"'
Autor:
Wang, Tianfu, Deng, Liwei, Wang, Chao, Lian, Jianxun, Yan, Yue, Yuan, Nicholas Jing, Zhang, Qi, Xiong, Hui
As the non-fungible token (NFT) market flourishes, price prediction emerges as a pivotal direction for investors gaining valuable insight to maximize returns. However, existing works suffer from a lack of practical definitions and standardized evalua
Externí odkaz:
http://arxiv.org/abs/2405.10640
Autor:
Wang, Tianfu, Fan, Qilin, Wang, Chao, Yang, Long, Ding, Leilei, Yuan, Nicholas Jing, Xiong, Hui
Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution t
Externí odkaz:
http://arxiv.org/abs/2404.12633
Autor:
He, Huiguo, Wang, Tianfu, Yang, Huan, Fu, Jianlong, Yuan, Nicholas Jing, Yin, Jian, Chao, Hongyang, Zhang, Qi
We study the task of generating profitable Non-Fungible Token (NFT) images from user-input texts. Recent advances in diffusion models have shown great potential for image generation. However, existing works can fall short in generating visually-pleas
Externí odkaz:
http://arxiv.org/abs/2306.11731
Autor:
Wu, Likang, Li, Zhi, Zhao, Hongke, Wang, Zhefeng, Liu, Qi, Huai, Baoxing, Yuan, Nicholas Jing, Chen, Enhong
Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new real-world knowledge for machines continuously. Recently, the Knowledge Graph (KG) has been proven as an effective sc
Externí odkaz:
http://arxiv.org/abs/2306.08487
Autor:
Ruan, Ludan, Ma, Yiyang, Yang, Huan, He, Huiguo, Liu, Bei, Fu, Jianlong, Yuan, Nicholas Jing, Jin, Qin, Guo, Baining
We propose the first joint audio-video generation framework that brings engaging watching and listening experiences simultaneously, towards high-quality realistic videos. To generate joint audio-video pairs, we propose a novel Multi-Modal Diffusion m
Externí odkaz:
http://arxiv.org/abs/2212.09478
Recent years have witnessed the improving performance of Chinese Named Entity Recognition (NER) from proposing new frameworks or incorporating word lexicons. However, the inner composition of entity mentions in character-level Chinese NER has been ra
Externí odkaz:
http://arxiv.org/abs/2204.05544
Autor:
Zhu, Xiangru, Li, Zhixu, Wang, Xiaodan, Jiang, Xueyao, Sun, Penglei, Wang, Xuwu, Xiao, Yanghua, Yuan, Nicholas Jing
Recent years have witnessed the resurgence of knowledge engineering which is featured by the fast growth of knowledge graphs. However, most of existing knowledge graphs are represented with pure symbols, which hurts the machine's capability to unders
Externí odkaz:
http://arxiv.org/abs/2202.05786
Autor:
Zhu, Tong, Qu, Xiaoye, Chen, Wenliang, Wang, Zhefeng, Huai, Baoxing, Yuan, Nicholas Jing, Zhang, Min
Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in both training and inference. In contrast to the previous studies, we pr
Externí odkaz:
http://arxiv.org/abs/2112.06013
Autor:
Zhang, Wenkai, Lin, Hongyu, Han, Xianpei, Sun, Le, Liu, Huidan, Wei, Zhicheng, Yuan, Nicholas Jing
Denoising is the essential step for distant supervision based named entity recognition. Previous denoising methods are mostly based on instance-level confidence statistics, which ignore the variety of the underlying noise distribution on different da
Externí odkaz:
http://arxiv.org/abs/2106.09234
This paper studies how to automatically generate a natural language text that describes the facts in knowledge graph (KG). Considering the few-shot setting, we leverage the excellent capacities of pretrained language models (PLMs) in language underst
Externí odkaz:
http://arxiv.org/abs/2106.01623