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Found 4 papers, 1 papers with code
DBGDGM: Dynamic Brain Graph Deep Generative Model
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.
DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning
Graph neural networks (GNNs) have demonstrated success in learning representations of brain graphs derived from functional magnetic resonance imaging (fMRI) data.
An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution
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.
High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm
Moreover, these pipelines are deterministic in nature, making them unable to capture predictive uncertainty.
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