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Course Name: NeuroData Design

Brainlit

Understanding the structure of the brain can lead to advances in treating neurological disorders. Our team is developing Brainlit, a...

Graph Statistics in the Brain

Inherent variability within a single network orĀ  populations of networks is an increasingly desirable phenomenon to characterize. Implications of this...

M2G

Connectomes are brain mappings that allow us to understand the variability in brain connectivity and brain function. Currently, there is...

Mouse

The connectome is a comprehensive map of the structural and functional connections of the brain, derived from multiple neuroimaging modalities....

mvlearn

In many datasets, there are multiple measurement modalities of the same subject, i.e. multiple X matrices (views) for the same...

Tealeaf

Random Forest (RF) is an interpretable and robust machine learning algorithm for classification and regression. However, existing RF methods are...

Improving Adversarial Task Transfer in Lifelong Learning Models via Domain Adaptation

Lifelong learning is a paradigm proposed to address catastrophic forgetting as new tasks are introduced and learned by a model....

RF/DN: Analysis of Random Forests and Deep Networks with Varying Sample Sizes

Random forests (RF) and deep networks (DN) are two of the primary machine learning methods in current literature, yet they...

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