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Found 11 papers, 6 papers with code
Neural Methods for Amortised Parameter Inference
Simulation-based methods for making statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements.
At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition
We also use our model to further explore landslide hazard for the same return periods under different climate change scenarios up to the end of the century.
Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks
Neural Bayes estimators are neural networks that approximate Bayes estimators in a fast and likelihood-free manner.
Deep graphical regression for jointly moderate and extreme Australian wildfires
Recent wildfires in Australia have led to considerable economic loss and property destruction, and there is increasing concern that climate change may exacerbate their intensity, duration, and frequency.
Flexible and efficient spatial extremes emulation via variational autoencoders
In this paper, we aim to push the boundaries on computation and modeling of high-dimensional spatial extremes via integrating a new spatial extremes model that has flexible and non-stationary dependence properties in the encoding-decoding structure of a variational autoencoder called the XVAE.
Neural Bayes estimators for censored inference with peaks-over-threshold models
Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods.
Insights into the drivers and spatio-temporal trends of extreme Mediterranean wildfires with statistical deep-learning
Our results highlight that whilst VPD, air temperature, and drought significantly affect wildfire occurrence, only VPD affects wildfire spread.
Likelihood-Free Parameter Estimation with Neural Bayes Estimators
Neural point estimators are neural networks that map data to parameter point estimates.
Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks
In this paper, we propose a new methodological framework for performing extreme quantile regression using artificial neutral networks, which are able to capture complex non-linear relationships and scale well to high-dimensional data.
Conex-Connect: Learning Patterns in Extremal Brain Connectivity From Multi-Channel EEG Data
Our proposed method, the conditional extremal dependence for brain connectivity (Conex-Connect), is a pioneering approach that links the association between extreme values of higher oscillations at a reference channel with the other brain network channels.
Modeling Non-Stationary Temperature Maxima Based on Extremal Dependence Changing with Event Magnitude
The modeling of spatio-temporal trends in temperature extremes can help better understand the structure and frequency of heatwaves in a changing climate.
Methodology Applications
