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Found 6 papers, 1 papers with code
Whole MILC: generalizing learned dynamics across tasks, datasets, and populations
In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC).
Fusing multimodal neuroimaging data with a variational autoencoder
Neuroimaging studies often involve the collection of multiple data modalities.
Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data
Our approach is evaluated on data with multiple motor sub-tasks to assess whether the model captures disentangled latent factors that correspond to each sub-task.
Geometrically Guided Integrated Gradients
We demonstrate through extensive experiments that the proposed approach outperforms vanilla and integrated gradients in subjective and quantitative assessment.
CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs
To analyze communication, the brain is often split up into anatomical regions that each perform certain computations.
I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey
In this paper, we survey I/O in ML applications on HPC systems, and target literature within a 6-year time window from 2019 to 2024.
