Zobrazeno 1 - 10
of 18 375
pro vyhledávání: '"Hassanzadeh, A. A."'
Autor:
Sun, Y. Qiang, Hassanzadeh, Pedram, Zand, Mohsen, Chattopadhyay, Ashesh, Weare, Jonathan, Abbot, Dorian S.
Predicting gray swan weather extremes, which are possible but so rare that they are absent from the training dataset, is a major concern for AI weather/climate models. An important open question is whether AI models can extrapolate from weaker weathe
Externí odkaz:
http://arxiv.org/abs/2410.14932
Autor:
Hassanzadeh, S. Hamid
This paper studies algebraic residual intersections in rings with Serre's condition \( S_{s} \). It demonstrates that residual intersections admit free approaches i.e. perfect subideal with the same radical. This fact leads to determining a uniform u
Externí odkaz:
http://arxiv.org/abs/2409.05705
Autor:
Guan, Yifei, Hassanzadeh, Pedram, Schneider, Tapio, Dunbar, Oliver, Huang, Daniel Zhengyu, Wu, Jinlong, Lopez-Gomez, Ignacio
Different approaches to using data-driven methods for subgrid-scale closure modeling have emerged recently. Most of these approaches are data-hungry, and lack interpretability and out-of-distribution generalizability. Here, we use {online} learning t
Externí odkaz:
http://arxiv.org/abs/2409.04985
Autor:
Hassanzadeh, Oktie
Recently, there has been an increasing interest in the construction of general-domain and domain-specific causal knowledge graphs. Such knowledge graphs enable reasoning for causal analysis and event prediction, and so have a range of applications ac
Externí odkaz:
http://arxiv.org/abs/2409.00331
Recent advances in deep learning structured state space models, especially the Mamba architecture, have demonstrated remarkable performance improvements while maintaining linear complexity. In this study, we introduce functional spatiotemporal Mamba
Externí odkaz:
http://arxiv.org/abs/2408.13074
Autor:
Bracco, Annalisa, Brajard, Julien, Dijkstra, Henk A., Hassanzadeh, Pedram, Lessig, Christian, Monteleoni, Claire
An exponential growth in computing power, which has brought more sophisticated and higher resolution simulations of the climate system, and an exponential increase in observations since the first weather satellite was put in orbit, are revolutionizin
Externí odkaz:
http://arxiv.org/abs/2408.09627
Machine learning (ML) techniques, especially neural networks (NNs), have shown promise in learning subgrid-scale parameterizations for climate models. However, a major problem with data-driven parameterizations, particularly those learned with superv
Externí odkaz:
http://arxiv.org/abs/2407.05224
Autor:
Li, Yanli, Hassanzadeh, Tahereh, Shamonin, Denis P., Reijnierse, Monique, Mil, Annette H. M. van der Helm-van, Stoel, Berend C.
Understanding the decisions of deep learning (DL) models is essential for the acceptance of DL to risk-sensitive applications. Although methods, like class activation maps (CAMs), give a glimpse into the black box, they do miss some crucial informati
Externí odkaz:
http://arxiv.org/abs/2407.01142
Autor:
Khatiwada, Aamod, Kokel, Harsha, Abdelaziz, Ibrahim, Chaudhury, Subhajit, Dolby, Julian, Hassanzadeh, Oktie, Huang, Zhenhan, Pedapati, Tejaswini, Samulowitz, Horst, Srinivas, Kavitha
Enterprises have a growing need to identify relevant tables in data lakes; e.g. tables that are unionable, joinable, or subsets of each other. Tabular neural models can be helpful for such data discovery tasks. In this paper, we present TabSketchFM,
Externí odkaz:
http://arxiv.org/abs/2407.01619
Generative approaches for cross-modality transformation have recently gained significant attention in neuroimaging. While most previous work has focused on case-control data, the application of generative models to disorder-specific datasets and thei
Externí odkaz:
http://arxiv.org/abs/2405.05462