Zobrazeno 1 - 10
of 216
pro vyhledávání: '"Ramachandran, Rahul"'
Autor:
Schmude, Johannes, Roy, Sujit, Trojak, Will, Jakubik, Johannes, Civitarese, Daniel Salles, Singh, Shraddha, Kuehnert, Julian, Ankur, Kumar, Gupta, Aman, Phillips, Christopher E, Kienzler, Romeo, Szwarcman, Daniela, Gaur, Vishal, Shinde, Rajat, Lal, Rohit, Da Silva, Arlindo, Diaz, Jorge Luis Guevara, Jones, Anne, Pfreundschuh, Simon, Lin, Amy, Sheshadri, Aditi, Nair, Udaysankar, Anantharaj, Valentine, Hamann, Hendrik, Watson, Campbell, Maskey, Manil, Lee, Tsengdar J, Moreno, Juan Bernabe, Ramachandran, Rahul
Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downs
Externí odkaz:
http://arxiv.org/abs/2409.13598
Autor:
Ramachandran, Rahul, Kulkarni, Tejal, Sharma, Charchit, Vijaykeerthy, Deepak, Balasubramanian, Vineeth N
Evaluating models and datasets in computer vision remains a challenging task, with most leaderboards relying solely on accuracy. While accuracy is a popular metric for model evaluation, it provides only a coarse assessment by considering a single mod
Externí odkaz:
http://arxiv.org/abs/2409.04041
Buckling instabilities driven by tissue growth underpin key developmental events such as the folding of the brain. Tissue growth is disordered due to cell-to-cell variability, but the effects of this variability on buckling are unknown. Here, we anal
Externí odkaz:
http://arxiv.org/abs/2407.07540
Global climate models typically operate at a grid resolution of hundreds of kilometers and fail to resolve atmospheric mesoscale processes, e.g., clouds, precipitation, and gravity waves (GWs). Model representation of these processes and their source
Externí odkaz:
http://arxiv.org/abs/2406.14775
Autor:
Bhattacharjee, Bishwaranjan, Trivedi, Aashka, Muraoka, Masayasu, Ramasubramanian, Muthukumaran, Udagawa, Takuma, Gurung, Iksha, Zhang, Rong, Dandala, Bharath, Ramachandran, Rahul, Maskey, Manil, Bugbee, Kaylin, Little, Mike, Fancher, Elizabeth, Sanders, Lauren, Costes, Sylvain, Blanco-Cuaresma, Sergi, Lockhart, Kelly, Allen, Thomas, Grezes, Felix, Ansdell, Megan, Accomazzi, Alberto, El-Kurdi, Yousef, Wertheimer, Davis, Pfitzmann, Birgit, Ramis, Cesar Berrospi, Dolfi, Michele, de Lima, Rafael Teixeira, Vagenas, Panagiotis, Mukkavilli, S. Karthik, Staar, Peter, Vahidinia, Sanaz, McGranaghan, Ryan, Mehrabian, Armin, Lee, Tsendgar
Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks. However, previous research demonstrated LLMs trained using domain-focused corpora perform better on specialized tasks
Externí odkaz:
http://arxiv.org/abs/2405.10725
Autor:
Walker, Kyle L., Partridge, Alix J., Chen, Hsing-Yu, Ramachandran, Rahul R., Stokes, Adam A., Tadakuma, Kenjiro, da Silva, Lucas Cruz, Giorgio-Serchi, Francesco
Stability and reliable operation under a spectrum of environmental conditions is still an open challenge for soft and continuum style manipulators. The inability to carry sufficient load and effectively reject external disturbances are two drawbacks
Externí odkaz:
http://arxiv.org/abs/2405.01925
Increasingly, more software services have been published onto the Internet, making it a big challenge to recommend services in the process of a scientific workflow composition. In this paper, a novel context-aware approach is proposed to recommending
Externí odkaz:
http://arxiv.org/abs/2404.00420
Autor:
Jakubik, Johannes, Roy, Sujit, Phillips, C. E., Fraccaro, Paolo, Godwin, Denys, Zadrozny, Bianca, Szwarcman, Daniela, Gomes, Carlos, Nyirjesy, Gabby, Edwards, Blair, Kimura, Daiki, Simumba, Naomi, Chu, Linsong, Mukkavilli, S. Karthik, Lambhate, Devyani, Das, Kamal, Bangalore, Ranjini, Oliveira, Dario, Muszynski, Michal, Ankur, Kumar, Ramasubramanian, Muthukumaran, Gurung, Iksha, Khallaghi, Sam, Hanxi, Li, Cecil, Michael, Ahmadi, Maryam, Kordi, Fatemeh, Alemohammad, Hamed, Maskey, Manil, Ganti, Raghu, Weldemariam, Kommy, Ramachandran, Rahul
Significant progress in the development of highly adaptable and reusable Artificial Intelligence (AI) models is expected to have a significant impact on Earth science and remote sensing. Foundation models are pre-trained on large unlabeled datasets t
Externí odkaz:
http://arxiv.org/abs/2310.18660
Autor:
Mukkavilli, S. Karthik, Civitarese, Daniel Salles, Schmude, Johannes, Jakubik, Johannes, Jones, Anne, Nguyen, Nam, Phillips, Christopher, Roy, Sujit, Singh, Shraddha, Watson, Campbell, Ganti, Raghu, Hamann, Hendrik, Nair, Udaysankar, Ramachandran, Rahul, Weldemariam, Kommy
Machine learning and deep learning methods have been widely explored in understanding the chaotic behavior of the atmosphere and furthering weather forecasting. There has been increasing interest from technology companies, government institutions, an
Externí odkaz:
http://arxiv.org/abs/2309.10808
Publikováno v:
Proceedings of The 24th IEEE/ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD2022-Winter)
As service-oriented architecture becoming one of the most prevalent techniques to rapidly deliver functionalities to customers, increasingly more reusable software components have been published online in forms of web services. To create a mashup, it
Externí odkaz:
http://arxiv.org/abs/2210.14127