Zobrazeno 1 - 10
of 226
pro vyhledávání: '"RAMACHANDRAN, RAHUL"'
Autor:
Shinde, Rajat, Phillips, Christopher E., Ankur, Kumar, Gupta, Aman, Pfreundschuh, Simon, Roy, Sujit, Kirkland, Sheyenne, Gaur, Vishal, Lin, Amy, Sheshadri, Aditi, Nair, Udaysankar, Maskey, Manil, Ramachandran, Rahul
High-quality machine learning (ML)-ready datasets play a foundational role in developing new artificial intelligence (AI) models or fine-tuning existing models for scientific applications such as weather and climate analysis. Unfortunately, despite t
Externí odkaz:
http://arxiv.org/abs/2412.02780
Autor:
Szwarcman, Daniela, Roy, Sujit, Fraccaro, Paolo, Gíslason, Þorsteinn Elí, Blumenstiel, Benedikt, Ghosal, Rinki, de Oliveira, Pedro Henrique, Almeida, Joao Lucas de Sousa, Sedona, Rocco, Kang, Yanghui, Chakraborty, Srija, Wang, Sizhe, Kumar, Ankur, Truong, Myscon, Godwin, Denys, Lee, Hyunho, Hsu, Chia-Yu, Asanjan, Ata Akbari, Mujeci, Besart, Keenan, Trevor, Arevalo, Paulo, Li, Wenwen, Alemohammad, Hamed, Olofsson, Pontus, Hain, Christopher, Kennedy, Robert, Zadrozny, Bianca, Cavallaro, Gabriele, Watson, Campbell, Maskey, Manil, Ramachandran, Rahul, Moreno, Juan Bernabe
This technical report presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2M global time series samples from NASA's Harmonized Landsat and Sentinel-2 data
Externí odkaz:
http://arxiv.org/abs/2412.02732
Autor:
Pantha, Nishan, Ramasubramanian, Muthukumaran, Gurung, Iksha, Maskey, Manil, Ramachandran, Rahul
The rapid development in large language models (LLMs) has transformed the landscape of natural language processing and understanding (NLP/NLU), offering significant benefits across various domains. However, when applied to scientific research, these
Externí odkaz:
http://arxiv.org/abs/2411.08181
Autor:
Roy, Sujit, Singh, Talwinder, Freitag, Marcus, Schmude, Johannes, Lal, Rohit, Hegde, Dinesha, Ranjan, Soumya, Lin, Amy, Gaur, Vishal, Vos, Etienne Eben, Ghosal, Rinki, Patro, Badri Narayana, Aydin, Berkay, Pogorelov, Nikolai, Moreno, Juan Bernabe, Maskey, Manil, Ramachandran, Rahul
Deep learning-based methods have been widely researched in the areas of language and vision, demonstrating their capacity to understand long sequences of data and their usefulness in numerous helio-physics applications. Foundation models (FMs), which
Externí odkaz:
http://arxiv.org/abs/2410.10841
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, Pantha, Nishan, Zhang, Rong, Dandala, Bharath, Ramachandran, Rahul, Maskey, Manil, Bugbee, Kaylin, Little, Mike, Fancher, Elizabeth, Gerasimov, Irina, Mehrabian, Armin, 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, 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