32 citations · 122 across the 33 of their papers we have counts for
4 papers · 1 filter
On the interplay of network structure and gradient convergence in deep learning
Vamsi K Ithapu, Sathya N Ravi, Vikas Singh
The regularization and output consistency behavior of dropout and layer-wise pretraining for learning deep networks have been fairly well studied. However, our understanding of how…
Convergence rates for pretraining and dropout: Guiding learning parameters using network structure
Vamsi K. Ithapu, Sathya Ravi, Vikas Singh
Unsupervised pretraining and dropout have been well studied, especially with respect to regularization and output consistency. However, our understanding about the explicit converg…
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…