6 citations · 6 across the 5 of their papers we have counts for
5 papers
DBGDGM: Dynamic Brain Graph Deep Generative Model
Alexander Campbell, Simeon Spasov, Nicola Toschi +1
Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as fun…
DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning
Alexander Campbell, Antonio Giuliano Zippo, Luca Passamonti +2
Graph neural networks (GNNs) have demonstrated success in learning representations of brain graphs derived from functional magnetic resonance imaging (fMRI) data. However, existing…
An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution
James King, Ramon Viñas Torné, Alexander Campbell +1
There have been several successful deep learning models that perform audio super-resolution. Many of these approaches involve using preprocessed feature extraction which requires a…
High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm
Lorena Qendro, Alexander Campbell, Pietro Liò +1
Electroencephalography (EEG) is crucial for the monitoring and diagnosis of brain disorders. However, EEG signals suffer from perturbations caused by non-cerebral artifacts limitin…
tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder
Alex Campbell, Pietro Liò
Variational autoencoders (VAEs) are a powerful class of deep generative latent variable model for unsupervised representation learning on high-dimensional data. To ensure computati…