Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Fernandez‐Granda, Carlos"'
Autor:
Crozier, Peter A., Leibovich, Matan, Haluai, Piyush, Tan, Mai, Thomas, Andrew M., Vincent, Joshua, Mohan, Sreyas, Morales, Adria Marcos, Kulkarni, Shreyas A., Matteson, David S., Wang, Yifan, Fernandez-Granda, Carlos
Nanoparticle surface structural dynamics is believed to play a significant role in regulating functionalities such as diffusion, reactivity, and catalysis but the atomic-level processes are not well understood. Atomic resolution characterization of n
Externí odkaz:
http://arxiv.org/abs/2407.17669
Autor:
Dheeshjith, Surya, Subel, Adam, Gupta, Shubham, Adcroft, Alistair, Fernandez-Granda, Carlos, Busecke, Julius, Zanna, Laure
With the success of machine learning (ML) applied to climate reaching further every day, emulators have begun to show promise not only for weather but for multi-year time scales in the atmosphere. Similar work for the ocean remains nascent, with stat
Externí odkaz:
http://arxiv.org/abs/2405.18585
Transformer-based detectors have shown success in computer vision tasks with natural images. These models, exemplified by the Deformable DETR, are optimized through complex engineering strategies tailored to the typical characteristics of natural sce
Externí odkaz:
http://arxiv.org/abs/2405.17677
Autor:
Yu, Boyang, Kaku, Aakash, Liu, Kangning, Parnandi, Avinash, Fokas, Emily, Venkatesan, Anita, Pandit, Natasha, Ranganath, Rajesh, Schambra, Heidi, Fernandez-Granda, Carlos
Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a novel framework to address this challenge, which leverages AI models trained exclusively on healthy individuals. The COnfidence-Based cha
Externí odkaz:
http://arxiv.org/abs/2311.12781
Autor:
de la Fuente, Jesus, Legarra, Naroa, Serrano, Guillermo, Marin-Goni, Irene, Diaz-Mazkiaran, Aintzane, Sendin, Markel Benito, Osta, Ana Garcia, Kalari, Krishna R., Fernandez-Granda, Carlos, Ochoa, Idoia, Hernaez, Mikel
Single-cell RNA-sequencing (scRNA-seq) stands as a powerful tool for deciphering cellular heterogeneity and exploring gene expression profiles at high resolution. However, its high cost renders it impractical for extensive sample cohorts within routi
Externí odkaz:
http://arxiv.org/abs/2311.11991
Publikováno v:
Journal of Advances in Modeling Earth Systems, 16, e2023MS004104
Ocean mesoscale eddies are often poorly represented in climate models, and therefore, their effects on the large scale circulation must be parameterized. Traditional parameterizations, which represent the bulk effect of the unresolved eddies, can be
Externí odkaz:
http://arxiv.org/abs/2311.02517
Autor:
Zhang, Cheng, Perezhogin, Pavel, Gultekin, Cem, Adcroft, Alistair, Fernandez-Granda, Carlos, Zanna, Laure
We address the question of how to use a machine learned parameterization in a general circulation model, and assess its performance both computationally and physically. We take one particular machine learned parameterization \cite{Guillaumin1&Zanna-J
Externí odkaz:
http://arxiv.org/abs/2303.00962
Subgrid parameterizations of mesoscale eddies continue to be in demand for climate simulations. These subgrid parameterizations can be powerfully designed using physics and/or data-driven methods, with uncertainty quantification. For example, Guillau
Externí odkaz:
http://arxiv.org/abs/2302.07984
Autor:
Li, Xiao, Liu, Sheng, Zhou, Jinxin, Lu, Xinyu, Fernandez-Granda, Carlos, Zhu, Zhihui, Qu, Qing
With the ever-increasing complexity of large-scale pre-trained models coupled with a shortage of labeled data for downstream training, transfer learning has become the primary approach in many fields, including natural language processing, computer v
Externí odkaz:
http://arxiv.org/abs/2212.12206
Empirical studies suggest that machine learning models trained with empirical risk minimization (ERM) often rely on attributes that may be spuriously correlated with the class labels. Such models typically lead to poor performance during inference fo
Externí odkaz:
http://arxiv.org/abs/2212.01433