27 citations · 28 across the 5 of their papers we have counts for
5 papers
Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse
Bradley T. Baker, Barak A. Pearlmutter, Robyn Miller +2
Our understanding of learning dynamics of deep neural networks (DNNs) remains incomplete. Recent research has begun to uncover the mathematical principles underlying these networks…
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis
Yutong Gao, Charles A. Ellis, Vince D. Calhoun +1
The high dimensionality and complexity of neuroimaging data necessitate large datasets to develop robust and high-performing deep learning models. However, the neuroimaging field i…
CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs
Eloy Geenjaar, Noah Lewis, Amrit Kashyap +2
Communication within or between complex systems is commonplace in the natural sciences and fields such as graph neural networks. The brain is a perfect example of such a complex sy…
Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data
Eloy Geenjaar, Amrit Kashyap, Noah Lewis +2
Functional magnetic resonance imaging (fMRI) data contain complex spatiotemporal dynamics, thus researchers have developed approaches that reduce the dimensionality of the signal w…
Algorithm-Agnostic Explainability for Unsupervised Clustering
Charles A. Ellis, Mohammad S. E. Sendi, Eloy P. T. Geenjaar +3
Supervised machine learning explainability has developed rapidly in recent years. However, clustering explainability has lagged behind. Here, we demonstrate the first adaptation of…