Zobrazeno 1 - 10
of 11 373
pro vyhledávání: '"Bhan, A."'
Autor:
Bhan, Nirav, Gupta, Shival, Manaswini, Sai, Baba, Ritik, Yadav, Narun, Desai, Hillori, Choudhary, Yash, Pawar, Aman, Shrivastava, Sarthak, Biswas, Sudipta
Large Language Models (LLMs) have shown remarkable capabilities in various domains, yet their economic impact has been limited by challenges in tool use and function calling. This paper introduces ThorV2, a novel architecture that significantly enhan
Externí odkaz:
http://arxiv.org/abs/2410.17950
In this work, we introduce a planning neural operator (PNO) for predicting the value function of a motion planning problem. We recast value function approximation as learning a single operator from the cost function space to the value function space,
Externí odkaz:
http://arxiv.org/abs/2410.17547
Publikováno v:
2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
This paper introduces a novel portable and scalable Active Noise Mitigation (PSANM) system designed to reduce low-frequency noise from construction machinery. The PSANM system consists of portable units with autonomous capabilities, optimized for sta
Externí odkaz:
http://arxiv.org/abs/2409.10534
Autor:
Lam, Bhan, Ong, Zhen-Ting, Ooi, Kenneth, Ong, Wen-Hui, Wong, Trevor, Watcharasupat, Karn N., Boey, Vanessa, Lee, Irene, Hong, Joo Young, Kang, Jian, Lee, Kar Fye Alvin, Christopoulos, Georgios, Gan, Woon-Seng
Publikováno v:
Building and Environment, vol. 266, p. 112106, Dec. 2024
Formalized in ISO 12913, the "soundscape" approach is a paradigmatic shift towards perception-based urban sound management, aiming to alleviate the substantial socioeconomic costs of noise pollution to advance the United Nations Sustainable Developme
Externí odkaz:
http://arxiv.org/abs/2407.05744
Neural operator approximations of the gain kernels in PDE backstepping has emerged as a viable method for implementing controllers in real time. With such an approach, one approximates the gain kernel, which maps the plant coefficient into the soluti
Externí odkaz:
http://arxiv.org/abs/2407.01745
Over the last decade, data-driven methods have surged in popularity, emerging as valuable tools for control theory. As such, neural network approximations of control feedback laws, system dynamics, and even Lyapunov functions have attracted growing a
Externí odkaz:
http://arxiv.org/abs/2405.11401
Autor:
Bhan, Milan, Vittaut, Jean-Noel, Achache, Nina, Legrand, Victor, Chesneau, Nicolas, Blangero, Annabelle, Murris, Juliette, Lesot, Marie-Jeanne
Toxicity mitigation consists in rephrasing text in order to remove offensive or harmful meaning. Neural natural language processing (NLP) models have been widely used to target and mitigate textual toxicity. However, existing methods fail to detoxify
Externí odkaz:
http://arxiv.org/abs/2405.09948
Incorporating natural language rationales in the prompt and In-Context Learning (ICL) have led to a significant improvement of Large Language Models (LLMs) performance. However, generating high-quality rationales require human-annotation or the use o
Externí odkaz:
http://arxiv.org/abs/2402.12038
Observers for PDEs are themselves PDEs. Therefore, producing real time estimates with such observers is computationally burdensome. For both finite-dimensional and ODE systems, moving-horizon estimators (MHE) are operators whose output is the state e
Externí odkaz:
http://arxiv.org/abs/2401.02516
To stabilize PDE models, control laws require space-dependent functional gains mapped by nonlinear operators from the PDE functional coefficients. When a PDE is nonlinear and its "pseudo-coefficient" functions are state-dependent, a gain-scheduling (
Externí odkaz:
http://arxiv.org/abs/2401.02511