Zobrazeno 1 - 10
of 15 115
pro vyhledávání: '"HUANG, CHAO"'
In this paper, we introduce a novel task called language-guided joint audio-visual editing. Given an audio and image pair of a sounding event, this task aims at generating new audio-visual content by editing the given sounding event conditioned on th
Externí odkaz:
http://arxiv.org/abs/2410.07463
Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user needs. However, existing RAG systems have signifi
Externí odkaz:
http://arxiv.org/abs/2410.05779
Model-Based Reward Shaping for Adversarial Inverse Reinforcement Learning in Stochastic Environments
Autor:
Zhan, Simon Sinong, Wu, Qingyuan, Wang, Philip, Wang, Yixuan, Jiao, Ruochen, Huang, Chao, Zhu, Qi
In this paper, we aim to tackle the limitation of the Adversarial Inverse Reinforcement Learning (AIRL) method in stochastic environments where theoretical results cannot hold and performance is degraded. To address this issue, we propose a novel met
Externí odkaz:
http://arxiv.org/abs/2410.03847
Autor:
Ye, Jiayi, Wang, Yanbo, Huang, Yue, Chen, Dongping, Zhang, Qihui, Moniz, Nuno, Gao, Tian, Geyer, Werner, Huang, Chao, Chen, Pin-Yu, Chawla, Nitesh V, Zhang, Xiangliang
LLM-as-a-Judge has been widely utilized as an evaluation method in various benchmarks and served as supervised rewards in model training. However, despite their excellence in many domains, potential issues are under-explored, undermining their reliab
Externí odkaz:
http://arxiv.org/abs/2410.02736
Autor:
Huang, Chao-Wei, Chen, Yun-Nung
Large language models have demonstrated significant potential as the next-generation information access engines. However, their reliability is hindered by issues of hallucination and generating non-factual content. This is particularly problematic in
Externí odkaz:
http://arxiv.org/abs/2410.01691
Autor:
Huang, Chao-Wei, Chen, Yun-Nung
Effective information retrieval (IR) from vast datasets relies on advanced techniques to extract relevant information in response to queries. Recent advancements in dense retrieval have showcased remarkable efficacy compared to traditional sparse ret
Externí odkaz:
http://arxiv.org/abs/2410.01383
Recently, a system identification method based on center manifold is proposed to identify polynomial nonlinear systems with uncontrollable linearization. This note presents a numerical example to show the effectiveness of this method.
Externí odkaz:
http://arxiv.org/abs/2410.02169
Autor:
Huang, Chao, Zang, Wenshuo, Pinciroli, Carlo, Li, Zhi Jane, Banerjee, Taposh, Su, Lili, Liu, Rui
Compared with single robots, Multi-Robot Systems (MRS) can perform missions more efficiently due to the presence of multiple members with diverse capabilities. However, deploying an MRS in wide real-world environments is still challenging due to unce
Externí odkaz:
http://arxiv.org/abs/2409.16577
Autor:
Lyu, Yanjun, Wu, Zihao, Zhang, Lu, Zhang, Jing, Li, Yiwei, Ruan, Wei, Liu, Zhengliang, Yu, Xiaowei, Cao, Chao, Chen, Tong, Chen, Minheng, Zhuang, Yan, Li, Xiang, Liu, Rongjie, Huang, Chao, Li, Wentao, Liu, Tianming, Zhu, Dajiang
Pre-trained large language models(LLMs) have attracted increasing attention in biomedical domains due to their success in natural language processing. However, the complex traits and heterogeneity of multi-sources genomics data pose significant chall
Externí odkaz:
http://arxiv.org/abs/2409.09825
Spatio-temporal prediction is a crucial research area in data-driven urban computing, with implications for transportation, public safety, and environmental monitoring. However, scalability and generalization challenges remain significant obstacles.
Externí odkaz:
http://arxiv.org/abs/2409.06748