no code implementations • 26 Jan 2023 • Alexander Campbell, Simeon Spasov, Nicola Toschi, Pietro Lio
In this paper, we propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings.
2 code implementations • 27 Sep 2022 • Alexander Campbell, Antonio Giuliano Zippo, Luca Passamonti, Nicola Toschi, Pietro Lio
Graph neural networks (GNNs) have demonstrated success in learning representations of brain graphs derived from functional magnetic resonance imaging (fMRI) data.
no code implementations • 30 Sep 2021 • James King, Ramon Viñas Torné, Alexander Campbell, Pietro Liò
Our paper compares the pre-upsampling AudioUNet to a new generative model that upsamples the signal before using deep learning to transform it into a more believable signal.
no code implementations • 21 Jul 2021 • Lorena Qendro, Alexander Campbell, Pietro Liò, Cecilia Mascolo
Moreover, these pipelines are deterministic in nature, making them unable to capture predictive uncertainty.