Max-infinitely divisible models and inference for spatial extremes

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Recent cosmic shear studies have shown that higher-order statistics (HOS) developed by independent teams now outperform standard two-point estimators in terms of statistical precision thanks to their sensitivity to the non-Gaussian features of large-scale ...

Activity-based models offer the potential of a far deeper understanding of daily mobility behaviour than trip-based models. However, activity-based models used both in research and practice have often relied on applying sequential choice models between sub ...

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

Point clouds are effective data structures for the rep- resentation of three-dimensional media and hence adopted in a wide range of practical applications. In many cases, the portrayed data is expected to be visualized by humans. After acquisition, point c ...

We introduce the elliptical Ornstein-Uhlenbeck (OU) process, which is a generalisation of the well-known univariate OU process to bivariate time series. This process maps out elliptical stochastic oscillations over time in the complex plane, which are obse ...

We introduce Neural Network (NN for short) approximation architectures for the numerical solution of Boundary Integral Equations (BIEs for short). We exemplify the proposed NN approach for the boundary reduction of the potential problem in two spatial dime ...

Two related methods for inverting line-integrated measurements are presented in this research paper in the context of the recent deuterium-tritium experiments in the JET tokamak. Unlike traditional methods of tomography, these methods rely on making use of ...

Natural language processing and other artificial intelligence fields have witnessed impressive progress over the past decade. Although some of this progress is due to algorithmic advances in deep learning, the majority has arguably been enabled by scaling ...

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

Recent successes in deep learning for vision and natural language processing are attributed to larger models but come with energy consumption and scalability issues. Current training of digital deep-learning models primarily relies on backpropagation that ...

Bedload transport often exhibits dual-mode behavior due to interactions of spatiotemporal controlling factors with the migrating three-dimensional bedforms (characterized by the fully developed patterns in the bed, such as alternate bars, pools, and cluste ...

These datasets contain the total particle number concentrations and normalized size distributions (dN/dlogDp) of excited, fluorescent, and hyper-fluorescent particles of sizes 0.5 to 20 μm (optical diameter). The normalized size distribution datasets are s ...

Max-infinitely divisible models and inference for spatial extremes

Chat with Graph Search

Euclid preparation: XXVIII. Forecasts for ten different higher-order weak lensing statistics

OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Towards a multiscale point cloud structural similarity metric

The elliptical Ornstein-Uhlenbeck process

ReLU Neural Network Galerkin BEM

Regressive Tomography and Direct Regressive Reconstruction: A Research Note

Text Representation Learning for Low Cost Natural Language Understanding

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Backpropagation-free training of deep physical neural networks

Dynamics of Dual-Mode Bedload Transport With Three-Dimensional Alternate Bars Migration in Subcritical Flow: Experiments and Model Analysis

Wideband integrated bioaerosol sensor (WIBS) excited, fluorescent, and hyper-fluorescent particle number concentrations and normalized size distributions (dN/dlogDp) measured in the Swiss container during MOSAiC 2019/2020