Zobrazeno 1 - 10
of 3 755
pro vyhledávání: '"ZENG, Zhen"'
In a Hilbert framework, we consider an inertial Tikhonov regularized dynamical system governed by a maximally comonotone operator, where the damping coefficient is proportional to the square root of the Tikhonov regularization parameter. Under an app
Externí odkaz:
http://arxiv.org/abs/2411.19693
Autor:
Verma, Gaurav, Kaur, Rachneet, Srishankar, Nishan, Zeng, Zhen, Balch, Tucker, Veloso, Manuela
State-of-the-art multimodal web agents, powered by Multimodal Large Language Models (MLLMs), can autonomously execute many web tasks by processing user instructions and interacting with graphical user interfaces (GUIs). Current strategies for buildin
Externí odkaz:
http://arxiv.org/abs/2411.13451
Knowledge editing aims to efficiently and cost-effectively correct inaccuracies and update outdated information. Recently, there has been growing interest in extending knowledge editing from Large Language Models (LLMs) to Multimodal Large Language M
Externí odkaz:
http://arxiv.org/abs/2411.12790
Recent advancements in language modeling have shown promising results when applied to time series data. In particular, fine-tuning pre-trained large language models (LLMs) for time series classification tasks has achieved state-of-the-art (SOTA) perf
Externí odkaz:
http://arxiv.org/abs/2407.06533
Travel planning is a complex task that involves generating a sequence of actions related to visiting places subject to constraints and maximizing some user satisfaction criteria. Traditional approaches rely on problem formulation in a given formal la
Externí odkaz:
http://arxiv.org/abs/2406.10196
Autor:
Lakkaraju, Kausik, Kaur, Rachneet, Zeng, Zhen, Zehtabi, Parisa, Patra, Sunandita, Srivastava, Biplav, Valtorta, Marco
AI systems are notorious for their fragility; minor input changes can potentially cause major output swings. When such systems are deployed in critical areas like finance, the consequences of their uncertain behavior could be severe. In this paper, w
Externí odkaz:
http://arxiv.org/abs/2406.12908
Autor:
Fons, Elizabeth, Kaur, Rachneet, Palande, Soham, Zeng, Zhen, Balch, Tucker, Veloso, Manuela, Vyetrenko, Svitlana
Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting, which is a critical task across many domains, spanning healthcare, finance, climate, energy, and many more. In this paper, we propose a framework for r
Externí odkaz:
http://arxiv.org/abs/2404.16563
Autor:
Lyu, Weiqiang, Tian, Huan, Fu, Zhenwei, Zhang, Lingjie, Zeng, Zhen, Zhang, Yaowen, Li, Heping, Zhang, Zhiyao, Liu, Yong
Broadband microwave waveforms with programmable chirp shapes are captivating in numerous practical applications. Compared with electronic technology, photonic-assisted solutions exhibit excellent performance in bandwidth and flexibility, but still su
Externí odkaz:
http://arxiv.org/abs/2404.12217
In order for robots to interact with objects effectively, they must understand the form and function of each object they encounter. Essentially, robots need to understand which actions each object affords, and where those affordances can be acted on.
Externí odkaz:
http://arxiv.org/abs/2404.11000
Autor:
Zeng, Zhen, Watson, William, Cho, Nicole, Rahimi, Saba, Reynolds, Shayleen, Balch, Tucker, Veloso, Manuela
The rapidly evolving field of Robotic Process Automation (RPA) has made significant strides in automating repetitive processes, yet its effectiveness diminishes in scenarios requiring spontaneous or unpredictable tasks demanded by users. This paper i
Externí odkaz:
http://arxiv.org/abs/2404.13050