Zobrazeno 1 - 10
of 404
pro vyhledávání: '"CHEN, Zixin"'
This paper explores the problem of class-generalizable anomaly detection, where the objective is to train one unified AD model that can generalize to detect anomalies in diverse classes from different domains without any retraining or fine-tuning on
Externí odkaz:
http://arxiv.org/abs/2410.20047
Autor:
Shum, KaShun, Xu, Minrui, Zhang, Jianshu, Chen, Zixin, Diao, Shizhe, Dong, Hanze, Zhang, Jipeng, Raza, Muhammad Omer
Large language models (LLMs) have become increasingly prevalent in our daily lives, leading to an expectation for LLMs to be trustworthy -- - both accurate and well-calibrated (the prediction confidence should align with its ground truth correctness
Externí odkaz:
http://arxiv.org/abs/2408.12168
The integration of Large Language Models (LLMs), especially ChatGPT, into education is poised to revolutionize students' learning experiences by introducing innovative conversational learning methodologies. To empower students to fully leverage the c
Externí odkaz:
http://arxiv.org/abs/2407.12423
Autor:
Zhu, Qian, Wang, Dakuo, Ma, Shuai, Wang, April Yi, Chen, Zixin, Khurana, Udayan, Ma, Xiaojuan
As AI technology continues to advance, the importance of human-AI collaboration becomes increasingly evident, with numerous studies exploring its potential in various fields. One vital field is data science, including feature engineering (FE), where
Externí odkaz:
http://arxiv.org/abs/2405.14107
Social media abounds with multimodal sarcasm, and identifying sarcasm targets is particularly challenging due to the implicit incongruity not directly evident in the text and image modalities. Current methods for Multimodal Sarcasm Target Identificat
Externí odkaz:
http://arxiv.org/abs/2405.00390
Autor:
Wang, Tiannan, Chen, Jiamin, Jia, Qingrui, Wang, Shuai, Fang, Ruoyu, Wang, Huilin, Gao, Zhaowei, Xie, Chunzhao, Xu, Chuou, Dai, Jihong, Liu, Yibin, Wu, Jialong, Ding, Shengwei, Li, Long, Huang, Zhiwei, Deng, Xinle, Yu, Teng, Ma, Gangan, Xiao, Han, Chen, Zixin, Xiang, Danjun, Wang, Yunxia, Zhu, Yuanyuan, Xiao, Yi, Wang, Jing, Wang, Yiru, Ding, Siran, Huang, Jiayang, Xu, Jiayi, Tayier, Yilihamu, Hu, Zhenyu, Gao, Yuan, Zheng, Chengfeng, Ye, Yueshu, Li, Yihang, Wan, Lei, Jiang, Xinyue, Wang, Yujie, Cheng, Siyu, Song, Zhule, Tang, Xiangru, Xu, Xiaohua, Zhang, Ningyu, Chen, Huajun, Jiang, Yuchen Eleanor, Zhou, Wangchunshu
This work introduces Weaver, our first family of large language models (LLMs) dedicated to content creation. Weaver is pre-trained on a carefully selected corpus that focuses on improving the writing capabilities of large language models. We then fin
Externí odkaz:
http://arxiv.org/abs/2401.17268
Autor:
Chen, Zixin, Liu, Shiyi, Jin, Zhihua, Huang, Gaoping, Chao, Yang, Yang, Zhenchuan, Li, Quan, Qu, Huamin
Multiplayer Online Battle Arenas (MOBAs) have gained a significant player base worldwide, generating over two billion US dollars in annual game revenue. However, the presence of griefers, who deliberately irritate and harass other players within the
Externí odkaz:
http://arxiv.org/abs/2312.14401
Autor:
He, Jianben, Wang, Xingbo, Wong, Kam Kwai, Huang, Xijie, Chen, Changjian, Chen, Zixin, Wang, Fengjie, Zhu, Min, Qu, Huamin
Constructing supervised machine learning models for real-world video analysis require substantial labeled data, which is costly to acquire due to scarce domain expertise and laborious manual inspection. While data programming shows promise in generat
Externí odkaz:
http://arxiv.org/abs/2308.00401
Autor:
Jin, Zhihua, Huang, Gaoping, Chen, Zixin, Liu, Shiyi, Chao, Yang, Yang, Zhenchuan, Li, Quan, Qu, Huamin
Multiplayer Online Battle Arenas (MOBAs) have garnered a substantial player base worldwide. Nevertheless, the presence of noxious players, commonly referred to as "actors", can significantly compromise game fairness by exhibiting negative behaviors t
Externí odkaz:
http://arxiv.org/abs/2307.09699
Publikováno v:
Proc. ACM Hum.-Comput. Interact. 6, CSCW2022
People often take user ratings and reviews into consideration when shopping for products or services online. However, such user-generated data contains self-selection bias that could affect people decisions and it is hard to resolve this issue comple
Externí odkaz:
http://arxiv.org/abs/2209.08751