7 citations · 13 across the 5 of their papers we have counts for
5 papers
Finding Differentially Covarying Needles in a Temporally Evolving Haystack: A Scan Statistics Perspective
Ronak Mehta, Hyunwoo J. Kim, Shulei Wang +3
Recent results in coupled or temporal graphical models offer schemes for estimating the relationship structure between features when the data come from related (but distinct) longi…
When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, -consistency and Neuroscience Applications
Hao Henry Zhou, Yilin Zhang, Vamsi K. Ithapu +3
Many studies in biomedical and health sciences involve small sample sizes due to logistic or financial constraints. Often, identifying weak (but scientifically interesting) associa…
On architectural choices in deep learning: From network structure to gradient convergence and parameter estimation
Vamsi K Ithapu, Sathya N Ravi, Vikas Singh
We study mechanisms to characterize how the asymptotic convergence of backpropagation in deep architectures, in general, is related to the network structure, and how it may be infl…
Convergence of gradient based pre-training in Denoising autoencoders
Vamsi K Ithapu, Sathya Ravi, Vikas Singh
The success of deep architectures is at least in part attributed to the layer-by-layer unsupervised pre-training that initializes the network. Various papers have reported extensiv…
Speeding up Permutation Testing in Neuroimaging
Chris Hinrichs, Vamsi K Ithapu, Qinyuan Sun +2
Multiple hypothesis testing is a significant problem in nearly all neuroimaging studies. In order to correct for this phenomena, we require a reliable estimate of the Family-Wise E…