2 code implementations • 22 May 2023 • Zhengxiang Shi, Francesco Tonolini, Nikolaos Aletras, Emine Yilmaz, Gabriella Kazai, Yunlong Jiao
Semi-supervised learning (SSL) is a popular setting aiming to effectively utilize unlabelled data to improve model performance in downstream natural language processing (NLP) tasks.
no code implementations • 7 Sep 2021 • Roderick Murray-Smith, John H. Williamson, Andrew Ramsay, Francesco Tonolini, Simon Rogers, Antoine Loriette
We infer finger 3D position $(x, y, z)$ and pose (pitch and yaw) on a mobile device using capacitive sensors which can sense the finger up to 5cm above the screen.
no code implementations • ICLR 2021 • Francesco Tonolini, Pablo G. Moreno, Andreas Damianou, Roderick Murray-Smith
We propose a new probabilistic method for unsupervised recovery of corrupted data.
no code implementations • 2 Dec 2019 • Alex Turpin, Gabriella Musarra, Valentin Kapitany, Francesco Tonolini, Ashley Lyons, Ilya Starshynov, Federica Villa, Enrico Conca, Francesco Fioranelli, Roderick Murray-Smith, Daniele Faccio
Traditional paradigms for imaging rely on the use of a spatial structure, either in the detector (pixels arrays) or in the illumination (patterned light).
2 code implementations • 13 Sep 2019 • Hunter Gabbard, Chris Messenger, Ik Siong Heng, Francesco Tonolini, Roderick Murray-Smith
Gravitational wave (GW) detection is now commonplace and as the sensitivity of the global network of GW detectors improves, we will observe $\mathcal{O}(100)$s of transient GW events per year.
no code implementations • ICLR 2019 • Francesco Tonolini, Bjorn Sand Jensen, Roderick Murray-Smith
We show that these sparse representations are advantageous over standard VAE representations on two benchmark classification tasks (MNIST and Fashion-MNIST) by demonstrating improved classification accuracy and significantly increased robustness to the number of latent dimensions.
no code implementations • 12 Apr 2019 • Francesco Tonolini, Jack Radford, Alex Turpin, Daniele Faccio, Roderick Murray-Smith
In such a way, Bayesian machine learning models can solve imaging inverse problems with minimal data collection efforts.