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Learning dynamical systems using dynamical systems : the reservoir computing approach

Verzelli, Pietro ; Alippi, Cesare (Dir.) ; Livi, Lorenzo (Codir.)

Thèse de doctorat : Università della Svizzera italiana, 2022 ; 2022INF001.

Dynamical systems have been used to describe a vast range of phenomena, including physical sciences, biology, neurosciences, and economics just to name a few. The development of a mathematical theory for dynamical systems allowed researchers to create precise models of many phenomena, predicting their behaviors with great accuracy. For many challenges of dynamical systems, highly accurate...

Graph neural networks : operators and architectures

Grattarola, Daniele ; Alippi, Cesare (Dir.) ; Livi, Lorenzo (Codir.)

Thèse de doctorat : Università della Svizzera italiana, 2021 ; 2021INFO014.

This thesis explores the field of graph neural networks, a class of deep learning models designed to learn representations of graphs. We organise the work into two parts. In the first part, we focus on the essential building blocks of graph neural networks. We present three novel operators for learning graph representations: one graph convolutional layer and two methods for pooling. We put...

Probabilistic models with deep neural networks

Masegosa, Andrés R. ; Cabañas, Rafael ; Langseth, Helge ; Nielsen, Thomas D. ; Salmerón, Antonio

In: Entropy, 2021, vol. 23, no. 1, p. 27 p

Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference is feasible. However, developments in variational inference, a general form of approximate probabilistic inference that originated in statistical...

Bayesian inference on stochastic gene transcription from flow cytometry data

Tiberi, Simone ; Walsh, Mark ; Cavallaro, Massimo ; Hebenstreit, Daniel ; Finkenstädt, Bärbel

In: Bioinformatics, 2018, vol. 34, no. 17, p. i647-i655

Integrating Intermittent Renewable Wind Generation - A Stochastic Multi-Market Electricity Model for the European Electricity Market

Abrell, Jan ; Kunz, Friedrich

In: Networks and Spatial Economics, 2015, vol. 15, no. 1, p. 117-147

A co-expression gene network associated with developmental regulation of apple fruit acidity

Bai, Yang ; Dougherty, Laura ; Cheng, Lailiang ; Xu, Kenong

In: Molecular Genetics and Genomics, 2015, vol. 290, no. 4, p. 1247-1263

SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles

Papili Gao, Nan ; Ud-Dean, S M Minhaz ; Gandrillon, Olivier ; Gunawan, Rudiyanto ; Valencia, Alfonso ; Valencia, Alfonso

In: Bioinformatics, 2018, vol. 34, no. 2, p. 258-266

Career difficulties and strategies of female self-initiated expatriates and self-initiated expatriate couples in Switzerland

Mancini-Vonlanthen, Nathalie ; Davoine, Eric (Dir.)

Thèse de doctorat : Université de Fribourg, 2021.

The impact of goal-directed motivational versus instructional self-talk on the lift performance of skilled CrossFit Athletes

N’Diaye, Malick ; Lorenzetti, Silvio (Dir.) ; Birrer, Daniel (Codir.) ; Horvath, Stephan (Codir.)

Mémoire de master : Université de Fribourg, 2020.

Advanced metaheuristics for the probabilistic orienteering problem

Chou, Xiaochen ; Gambardella, Luca Maria (Dir.) ; Montemanni, Roberto (Codir.)

Thèse de doctorat : Università della Svizzera italiana, 2020 ; 2020INFO020.

Stochastic Optimization Problems take uncertainty into account. For this reason they are in general more realistic than deterministic ones, meanwhile, more difficult to solve. The challenge is both on modelling and computation aspects: exact methods usually work only for small instances, besides, there are several problems with no closed-form expression or hard- to-compute objective functions....