Zobrazeno 1 - 10
of 38 325
pro vyhledávání: '"A, Keung"'
Dimension reduction techniques, such as Sufficient Dimension Reduction (SDR), are indispensable for analyzing high-dimensional datasets. This paper introduces a novel SDR method named Principal Square Response Forward Regression (PSRFR) for estimatin
Externí odkaz:
http://arxiv.org/abs/2409.02372
In this paper, we propose a physics-informed learning-based Koopman modeling approach and present a Koopman-based self-tuning moving horizon estimation design for a class of nonlinear systems. Specifically, we train Koopman operators and two neural n
Externí odkaz:
http://arxiv.org/abs/2408.03653
Autor:
Wong, Albert, Cheng, Florence Wing Yau, Keung, Ashley, Hercules, Yamileth, Garcia, Mary Alexandra, Lim, Yew-Wei, Pham, Lien
Topic modelling has become increasingly popular for summarizing text data, such as social media posts and articles. However, topic modelling is usually completed in one shot. Assessing the quality of resulting topics is challenging. No effective meth
Externí odkaz:
http://arxiv.org/abs/2407.17892
The increasing use of Large Language Models (LLMs) in software development has garnered significant attention from researchers assessing the quality of the code they generate. However, much of the research focuses on controlled datasets such as Human
Externí odkaz:
http://arxiv.org/abs/2406.19544
Autor:
Zhang, Fengji, Zhang, Zexian, Keung, Jacky Wai, Tang, Xiangru, Yang, Zhen, Yu, Xiao, Hu, Wenhua
Code Smell Detection (CSD) plays a crucial role in improving software quality and maintainability. And Deep Learning (DL) techniques have emerged as a promising approach for CSD due to their superior performance. However, the effectiveness of DL-base
Externí odkaz:
http://arxiv.org/abs/2406.19240
Recent conditional 3D completion works have mainly relied on CLIP or BERT to encode textual information, which cannot support complex instruction. Meanwhile, large language models (LLMs) have shown great potential in multi-modal understanding and gen
Externí odkaz:
http://arxiv.org/abs/2406.05543
Autor:
Liu, Xinhang, Tai, Yu-Wing, Tang, Chi-Keung, Miraldo, Pedro, Lohit, Suhas, Chatterjee, Moitreya
Extensions of Neural Radiance Fields (NeRFs) to model dynamic scenes have enabled their near photo-realistic, free-viewpoint rendering. Although these methods have shown some potential in creating immersive experiences, two drawbacks limit their ubiq
Externí odkaz:
http://arxiv.org/abs/2406.03723
Recent advancements in Multimodal Large Language Models (MM-LLMs) have demonstrated promising potential in terms of generalization and robustness when applied to different modalities. While previous works have already achieved 3D human motion generat
Externí odkaz:
http://arxiv.org/abs/2405.17013
We introduce C3LLM (Conditioned-on-Three-Modalities Large Language Models), a novel framework combining three tasks of video-to-audio, audio-to-text, and text-to-audio together. C3LLM adapts the Large Language Model (LLM) structure as a bridge for al
Externí odkaz:
http://arxiv.org/abs/2405.16136
With the increasing utilization of large language models such as ChatGPT during software development, it has become crucial to verify the quality of code content it generates. Recent studies proposed utilizing ChatGPT as both a developer and tester f
Externí odkaz:
http://arxiv.org/abs/2405.12641