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 or 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...