Zobrazeno 1 - 10
of 4 926
pro vyhledávání: '"Malviya, P."'
The optimal model for a given task is often challenging to determine, requiring training multiple models from scratch which becomes prohibitive as dataset and model sizes grow. A more efficient alternative is to reuse smaller pre-trained models by ex
Externí odkaz:
http://arxiv.org/abs/2405.15895
Autor:
Das, Arindam, Paul, Sudarshan, Scholz, Niko, Malviya, Akhilesh Kumar, Sistu, Ganesh, Bhattacharya, Ujjwal, Eising, Ciarán
Accurate obstacle identification represents a fundamental challenge within the scope of near-field perception for autonomous driving. Conventionally, fisheye cameras are frequently employed for comprehensive surround-view perception, including rear-v
Externí odkaz:
http://arxiv.org/abs/2402.00637
Autor:
Malviya-Thakur, Addi, Milewicz, Reed, Paganini, Lavinia, Mahmoud, Ahmed Samir Imam, Mockus, Audris
The proliferation of open-source scientific software for science and research presents opportunities and challenges. In this paper, we introduce the SciCat dataset -- a comprehensive collection of Free-Libre Open Source Software (FLOSS) projects, des
Externí odkaz:
http://arxiv.org/abs/2312.06382
Autor:
McInnes, Lois Curfman, Heroux, Michael, Bernholdt, David E., Dubey, Anshu, Gonsiorowski, Elsa, Gupta, Rinku, Marques, Osni, Moulton, J. David, Nam, Hai Ah, Norris, Boyana, Raybourn, Elaine M., Willenbring, Jim, Almgren, Ann, Bartlett, Ross, Cranfill, Kita, Fickas, Stephen, Frederick, Don, Godoy, William, Grubel, Patricia, Hartman-Baker, Rebecca, Huebl, Axel, Lynch, Rose, Thakur, Addi Malviya, Milewicz, Reed, Miller, Mark C., Mundt, Miranda, Palmer, Erik, Parete-Koon, Suzanne, Phinney, Megan, Riley, Katherine, Rogers, David M., Sims, Ben, Stevens, Deborah, Watson, Gregory R.
Computational and data-enabled science and engineering are revolutionizing advances throughout science and society, at all scales of computing. For example, teams in the U.S. DOE Exascale Computing Project have been tackling new frontiers in modeling
Externí odkaz:
http://arxiv.org/abs/2311.02010
Autor:
Watson, Gregory R., Malviya-Thakur, Addi, Katz, Daniel S., Raybourn, Elaine M., Hoffman, Bill, Robinson, Dana, Kellerman, John, Roundy, Clark
Research software plays a crucial role in advancing scientific knowledge, but ensuring its sustainability, maintainability, and long-term viability is an ongoing challenge. The Sustainable Research Software Institute (SRSI) Model has been designed to
Externí odkaz:
http://arxiv.org/abs/2308.14954
Autor:
Watson, Gregory R., Malviya-Thakur, Addi, Katz, Daniel S., Raybourn, Elaine M., Hoffman, Bill, Robinson, Dana, Kellerman, John, Roundy, Clark
Research software plays a crucial role in advancing scientific knowledge, but ensuring its sustainability, maintainability, and long-term viability is an ongoing challenge. To address these concerns, the Sustainable Research Software Institute (SRSI)
Externí odkaz:
http://arxiv.org/abs/2308.14953
Publikováno v:
Journal of Asian Ceramic Societies, Pp 1-16 (2024)
A statistically designed property database of six commercial glass families is leveraged to develop models relating viscosity to temperature and composition. Specialized models for each glass family were designed based on the Adam–Gibbs equation. W
Externí odkaz:
https://doaj.org/article/aeaf48ba38024365b8f7908703add253
Autor:
Harsh G. Kamath, Manmeet Singh, Neetiraj Malviya, Alberto Martilli, Liu He, Daniel Aliaga, Cenlin He, Fei Chen, Lori A. Magruder, Zong-Liang Yang, Dev Niyogi
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-15 (2024)
Abstract We introduce University of Texas - GLObal Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for more than 1200 city or locales worldwide. UT-GLOBUS combines open-source sp
Externí odkaz:
https://doaj.org/article/7df6f5e973cc40968ae05eef6162381a
Sharpness-aware minimization (SAM) methods have gained increasing popularity by formulating the problem of minimizing both loss value and loss sharpness as a minimax objective. In this work, we increase the efficiency of the maximization and minimiza
Externí odkaz:
http://arxiv.org/abs/2307.16704
Autor:
Malviya, Pranshu, Mordido, Gonçalo, Baratin, Aristide, Harikandeh, Reza Babanezhad, Huang, Jerry, Lacoste-Julien, Simon, Pascanu, Razvan, Chandar, Sarath
Adaptive gradient-based optimizers, notably Adam, have left their mark in training large-scale deep learning models, offering fast convergence and robustness to hyperparameter settings. However, they often struggle with generalization, attributed to
Externí odkaz:
http://arxiv.org/abs/2307.09638