Zobrazeno 1 - 10
of 1 560
pro vyhledávání: '"Patel, Harsh"'
Autor:
Ahn, Michael, Arenas, Montserrat Gonzalez, Bennice, Matthew, Brown, Noah, Chan, Christine, David, Byron, Francis, Anthony, Gonzalez, Gavin, Hessmer, Rainer, Jackson, Tomas, Joshi, Nikhil J, Lam, Daniel, Lee, Tsang-Wei Edward, Luong, Alex, Maddineni, Sharath, Patel, Harsh, Peralta, Jodilyn, Quiambao, Jornell, Reyes, Diego, Ruano, Rosario M Jauregui, Sadigh, Dorsa, Sanketi, Pannag, Takayama, Leila, Vodenski, Pavel, Xia, Fei
Robots today can exploit the rich world knowledge of large language models to chain simple behavioral skills into long-horizon tasks. However, robots often get interrupted during long-horizon tasks due to primitive skill failures and dynamic environm
Externí odkaz:
http://arxiv.org/abs/2405.16021
Autor:
Patel, Harsh, Ramanan, Buvaneswari A., Khan, Manzoor A., Williams, Thomas, Friedman, Brian, Drabeck, Lawrence
This paper explores the possibilities of the current generation of Large Language Models for incorporating Machine Learning Operations (MLOps) functionalities into ML training code bases. We evaluate the performance of OpenAI (gpt-3.5-turbo) and Wiza
Externí odkaz:
http://arxiv.org/abs/2405.06835
Autor:
Patel, Harsh
As electric vehicles (EVs) are seen as the future of transportation, there are two significant challenges to overcome: range and cost. One effective strategy to address these issues is the optimization of powertrain components, which significantly im
Externí odkaz:
http://hdl.handle.net/11375/30126
This paper investigates the complexities of integrating Large Language Models (LLMs) into software products, with a focus on the challenges encountered for determining their readiness for release. Our systematic review of grey literature identifies c
Externí odkaz:
http://arxiv.org/abs/2403.18958
This article addresses the pump-scheduling optimization problem to enhance real-time control of real-world water distribution networks (WDNs). Our primary objectives are to adhere to physical operational constraints while reducing energy consumption
Externí odkaz:
http://arxiv.org/abs/2310.09412
Autor:
Sahni, Shivam, Patel, Harsh
With the rise of deep learning, large datasets and complex models have become common, requiring significant computing power. To address this, data distillation has emerged as a technique to quickly train models with lower memory and time requirements
Externí odkaz:
http://arxiv.org/abs/2308.04982
Autor:
Rajak, Kamal Kishor, Pahilani, Pavan, Patel, Harsh, Kikani, Bhavtosh, Desai, Rucha, Kumar, Hemant
Silver nanoparticles (AgNP's) possess inherent biological potentials that have obliged an alternative, eco-friendly, sustainable approach to "Green Synthesis." In the present study, we synthesized Green Silver Nanoparticles (GAgNP's) using Curcuma lo
Externí odkaz:
http://arxiv.org/abs/2304.04777
Visualizations such as bar charts, scatter plots, and objects on geographical maps often convey critical information, including exact and relative numeric values, using shapes. The choice of shape and method of encoding information is often arbitrari
Externí odkaz:
http://arxiv.org/abs/2211.05965
Autor:
Ondov, Brian, Patel, Harsh B., Kuo, Ai-Te, Samet, Hanan, Kastner, John, Han, Yunheng, Wei, Hong, Elmqvist, Niklas
While many dashboards for visualizing COVID-19 data exist, most separate geospatial and temporal data into discrete visualizations or tables. Further, the common use of choropleth maps or space-filling map overlays supports only a single geospatial v
Externí odkaz:
http://arxiv.org/abs/2211.05823
Autor:
Patel, Harsh, Sahni, Shivam
With the growing use of deep learning methods, particularly graph neural networks, which encode intricate interconnectedness information, for a variety of real tasks, there is a necessity for explainability in such settings. In this paper, we demonst
Externí odkaz:
http://arxiv.org/abs/2211.01770