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pro vyhledávání: '"Risser A"'
Extreme events over large spatial domains may exhibit highly heterogeneous tail dependence characteristics, yet most existing spatial extremes models yield only one dependence class over the entire spatial domain. To accurately characterize "data-lev
Externí odkaz:
http://arxiv.org/abs/2412.07957
The Gaussian process (GP) is a widely used probabilistic machine learning method for stochastic function approximation, stochastic modeling, and analyzing real-world measurements of nonlinear processes. Unlike many other machine learning methods, GPs
Externí odkaz:
http://arxiv.org/abs/2411.05869
Climate change detection and attribution (D&A) is concerned with determining the extent to which anthropogenic activities have influenced specific aspects of the global climate system. D&A fits within the broader field of causal inference, the collec
Externí odkaz:
http://arxiv.org/abs/2408.16004
The last decade has seen numerous record-shattering heatwaves in all corners of the globe. In the aftermath of these devastating events, there is interest in identifying worst-case thresholds or upper bounds that quantify just how hot temperatures ca
Externí odkaz:
http://arxiv.org/abs/2408.10251
Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators
Autor:
Mahesh, Ankur, Collins, William, Bonev, Boris, Brenowitz, Noah, Cohen, Yair, Elms, Joshua, Harrington, Peter, Kashinath, Karthik, Kurth, Thorsten, North, Joshua, OBrien, Travis, Pritchard, Michael, Pruitt, David, Risser, Mark, Subramanian, Shashank, Willard, Jared
Studying low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampl
Externí odkaz:
http://arxiv.org/abs/2408.03100
Autor:
Mahesh, Ankur, Collins, William, Bonev, Boris, Brenowitz, Noah, Cohen, Yair, Harrington, Peter, Kashinath, Karthik, Kurth, Thorsten, North, Joshua, OBrien, Travis, Pritchard, Michael, Pruitt, David, Risser, Mark, Subramanian, Shashank, Willard, Jared
In Part I, we created an ensemble based on Spherical Fourier Neural Operators. As initial condition perturbations, we used bred vectors, and as model perturbations, we used multiple checkpoints trained independently from scratch. Based on diagnostics
Externí odkaz:
http://arxiv.org/abs/2408.01581
Publikováno v:
Adolescent Health, Medicine and Therapeutics, Vol Volume 8, Pp 87-94 (2017)
William L Risser,1 Jan M Risser,2 Amanda L Risser3 1Department of Pediatrics, University of Texas Medical School, 2Division of Epidemiology, University of Texas School of Public Health, Houston, TX, 3Department of Family Medicine, Oregon Health and S
Externí odkaz:
https://doaj.org/article/e2bc20bd27c9437a96ba0048ce1ef748
Autor:
Brotto, Renan D. B., Loubes, Jean-Michel, Risser, Laurent, Florens, Jean-Pierre, Nose-Filho, Kenji, Romano, João M. T.
We tackle the problem of bias mitigation of algorithmic decisions in a setting where both the output of the algorithm and the sensitive variable are continuous. Most of prior work deals with discrete sensitive variables, meaning that the biases are m
Externí odkaz:
http://arxiv.org/abs/2402.15477
Autor:
Menes, Thibaut, Risser-Maroix, Olivier
Model Soups, extending Stochastic Weights Averaging (SWA), combine models fine-tuned with different hyperparameters. Yet, their adoption is hindered by computational challenges due to subset selection issues. In this paper, we propose to speed up mod
Externí odkaz:
http://arxiv.org/abs/2401.17790
Ensuring fairness in NLP models is crucial, as they often encode sensitive attributes like gender and ethnicity, leading to biased outcomes. Current concept erasure methods attempt to mitigate this by modifying final latent representations to remove
Externí odkaz:
http://arxiv.org/abs/2312.06499