In: Algorithms, 2020, vol. 13, no. 12, p. 17
Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations. BN parameter learning from incomplete data is usually implemented with the Expectation-Maximisation algorithm (EM), which computes the relevant sufficient statistics (“soft EM”) ...
|
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...
|
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...
|
Thèse de doctorat : Università della Svizzera italiana, 2021 ; 2021INFO011.
Personal computing systems like e.g., laptop, smartphone, and smartwatches are nowadays ubiquitous in people's everyday life. People use such systems not only for communicating or searching for information, but also as digital companions, able to track and support their daily activities such as sleep, food intake, physical exercise and even work. Sensors embedded in personal computing systems...
|
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...
|
In: Bioinformatics, 2018, vol. 34, no. 8, p. 1433-1435
|
In: International Journal of Computer Vision, 2015, vol. 114, no. 2-3, p. 306-321
|
In: tm - Technisches Messen, 2017, vol. 84, no. 7-8, p. 502-511
|
In: Neuroinformatics, 2015, vol. 13, no. 1, p. 83-92
|
In: Advanced Optical Technologies, 2017, vol. 6, no. 6, p. 439-448
|