no code implementations • pproximateinference AABI Symposium 2022 • Giorgos Felekis, Theo Damoulas, Brooks Paige
We study probabilistic Deep Learning methods through the lens of Approximate Bayesian Inference.
no code implementations • 19 Jul 2018 • Adam Tsakalidis, Maria Liakata, Theo Damoulas, Alexandra I. Cristea
Predicting mental health from smartphone and social media data on a longitudinal basis has recently attracted great interest, with very promising results being reported across many studies.
no code implementations • 4 Apr 2018 • Karla Monterrubio-Gómez, Lassi Roininen, Sara Wade, Theo Damoulas, Mark Girolami
Gaussian processes are valuable tools for non-parametric modelling, where typically an assumption of stationarity is employed.
Computation
no code implementations • COLING 2016 • Adam Tsakalidis, Maria Liakata, Theo Damoulas, Brigitte Jellinek, Weisi Guo, Alex Cristea, ra
In this paper we address a new problem of predicting affect and well-being scales in a real-world setting of heterogeneous, longitudinal and non-synchronous textual as well as non-linguistic data that can be harvested from on-line media and mobile phones.