Zobrazeno 1 - 10
of 3 392
pro vyhledávání: '"Maskey A"'
Autor:
Wang ZZ, Li H, Maskey AR, Srivastava K, Liu C, Yang N, Xie T, Fu Z, Li J, Liu X, Sampson HA, Li XM
Publikováno v:
Journal of Inflammation Research, Vol Volume 17, Pp 2547-2561 (2024)
Zhen-Zhen Wang,1– 3,* Hang Li,4,* Anish R Maskey,2,* Kamal Srivastava,2,5,* Changda Liu,6,* Nan Yang,2,5 Taoyun Xie,7 Ziyi Fu,8 Junxiong Li,9 Xiaohong Liu,10 Hugh A Sampson,6 Xiu-Min Li2,11 1Academy of Chinese Medical Science, H
Externí odkaz:
https://doaj.org/article/e6694da8800f40ac974467f06690793d
Publikováno v:
Journal of Inflammation Research, Vol Volume 15, Pp 5527-5540 (2022)
Anish Maskey,1 Kamal Srivastava,1,2 Gary Soffer,3 David Dunkin,4 Qian Yuan,5 Xiu-Min Li1,6 1Department of Pathology, Microbiology & Immunology, New York Medical College, Valhalla, NY, USA; 2General Nutraceutical Technology, LLC, Elmsford, NY, USA; 3D
Externí odkaz:
https://doaj.org/article/2ceb8a42b0a5412e8f26403bb40b1d26
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
We study the generalization capabilities of Message Passing Neural Networks (MPNNs), a prevalent class of Graph Neural Networks (GNN). We derive generalization bounds specifically for MPNNs with normalized sum aggregation and mean aggregation. Our an
Externí odkaz:
http://arxiv.org/abs/2404.03473
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
We introduce $r$-loopy Weisfeiler-Leman ($r$-$\ell{}$WL), a novel hierarchy of graph isomorphism tests and a corresponding GNN framework, $r$-$\ell{}$MPNN, that can count cycles up to length $r + 2$. Most notably, we show that $r$-$\ell{}$WL can coun
Externí odkaz:
http://arxiv.org/abs/2403.13749
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