27 citations · 181 across the 39 of their papers we have counts for
21 papers · 1 filter
Spectral Introspection Identifies Group Training Dynamics in Deep Neural Networks for Neuroimaging
Bradley T. Baker, Vince D. Calhoun, Sergey M. Plis
Neural networks, whice have had a profound effect on how researchers study complex phenomena, do so through a complex, nonlinear mathematical structure which can be difficult for h…
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…
Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey
Md. Mahfuzur Rahman, Vince D. Calhoun, Sergey M. Plis
Deep learning (DL) models have been popular due to their ability to learn directly from the raw data in an end-to-end paradigm, alleviating the concern of a separate error-prone fe…
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes
Alex Fedorov, Eloy Geenjaar, Lei Wu +7
Recent neuroimaging studies that focus on predicting brain disorders via modern machine learning approaches commonly include a single modality and rely on supervised over-parameter…
Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series
Usman Mahmood, Zening Fu, Vince Calhoun +1
Recently, methods that represent data as a graph, such as graph neural networks (GNNs) have been successfully used to learn data representations and structures to solve classificat…
Single-Shot Pruning for Offline Reinforcement Learning
Samin Yeasar Arnob, Riyasat Ohib, Sergey Plis +1
Deep Reinforcement Learning (RL) is a powerful framework for solving complex real-world problems. Large neural networks employed in the framework are traditionally associated with…