Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Maskey, Manil"'
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
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:
Jain, Nitisha, Akhtar, Mubashara, Giner-Miguelez, Joan, Shinde, Rajat, Vanschoren, Joaquin, Vogler, Steffen, Goswami, Sujata, Rao, Yuhan, Santos, Tim, Oala, Luis, Karamousadakis, Michalis, Maskey, Manil, Marcenac, Pierre, Conforti, Costanza, Kuchnik, Michael, Aroyo, Lora, Benjelloun, Omar, Simperl, Elena
Data is critical to advancing AI technologies, yet its quality and documentation remain significant challenges, leading to adverse downstream effects (e.g., potential biases) in AI applications. This paper addresses these issues by introducing Croiss
Externí odkaz:
http://arxiv.org/abs/2407.16883
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
Identifying and handling label errors can significantly enhance the accuracy of supervised machine learning models. Recent approaches for identifying label errors demonstrate that a low self-confidence of models with respect to a certain label repres
Externí odkaz:
http://arxiv.org/abs/2405.09602
Autor:
Akhtar, Mubashara, Benjelloun, Omar, Conforti, Costanza, Gijsbers, Pieter, Giner-Miguelez, Joan, Jain, Nitisha, Kuchnik, Michael, Lhoest, Quentin, Marcenac, Pierre, Maskey, Manil, Mattson, Peter, Oala, Luis, Ruyssen, Pierre, Shinde, Rajat, Simperl, Elena, Thomas, Goeffry, Tykhonov, Slava, Vanschoren, Joaquin, van der Velde, Jos, Vogler, Steffen, Wu, Carole-Jean
Data is a critical resource for Machine Learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that simplifies how data is used by ML tools and frameworks. Croissant makes da
Externí odkaz:
http://arxiv.org/abs/2403.19546
Autor:
Oala, Luis, Maskey, Manil, Bat-Leah, Lilith, Parrish, Alicia, Gürel, Nezihe Merve, Kuo, Tzu-Sheng, Liu, Yang, Dror, Rotem, Brajovic, Danilo, Yao, Xiaozhe, Bartolo, Max, Rojas, William A Gaviria, Hileman, Ryan, Aliment, Rainier, Mahoney, Michael W., Risdal, Meg, Lease, Matthew, Samek, Wojciech, Dutta, Debojyoti, Northcutt, Curtis G, Coleman, Cody, Hancock, Braden, Koch, Bernard, Tadesse, Girmaw Abebe, Karlaš, Bojan, Alaa, Ahmed, Dieng, Adji Bousso, Noy, Natasha, Reddi, Vijay Janapa, Zou, James, Paritosh, Praveen, van der Schaar, Mihaela, Bollacker, Kurt, Aroyo, Lora, Zhang, Ce, Vanschoren, Joaquin, Guyon, Isabelle, Mattson, Peter
Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets that will adva
Externí odkaz:
http://arxiv.org/abs/2311.13028
Autor:
Ramasubramanian, Muthukumaran, Gurung, Iksha, Gahlot, Shubhankar, Hänsch, Ronny, Molthan, Andrew L., Maskey, Manil
Accurate detection of inundated water extents during flooding events is crucial in emergency response decisions and aids in recovery efforts. Satellite Remote Sensing data provides a global framework for detecting flooding extents. Specifically, Sent
Externí odkaz:
http://arxiv.org/abs/2311.09276
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
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