3 citations · 3 across the 2 of their papers we have counts for
4 papers
UnICORNN: A recurrent model for learning very long time dependencies
T. Konstantin Rusch, Siddhartha Mishra
The design of recurrent neural networks (RNNs) to accurately process sequential inputs with long-time dependencies is very challenging on account of the exploding and vanishing gra…
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies
T. Konstantin Rusch, Siddhartha Mishra
Circuits of biological neurons, such as in the functional parts of the brain can be modeled as networks of coupled oscillators. Inspired by the ability of these systems to express…
Higher-order Quasi-Monte Carlo Training of Deep Neural Networks
M. Longo, S. Mishra, T. K. Rusch +1
We present a novel algorithmic approach and an error analysis leveraging Quasi-Monte Carlo points for training deep neural network (DNN) surrogates of Data-to-Observable (DtO) maps…
Enhancing accuracy of deep learning algorithms by training with low-discrepancy sequences
Siddhartha Mishra, T. Konstantin Rusch
We propose a deep supervised learning algorithm based on low-discrepancy sequences as the training set. By a combination of theoretical arguments and extensive numerical experiment…