Matrix Inference and Estimation in Multi-Layer Models

1 code implementation • NeurIPS 2020 • Parthe Pandit, Mojtaba Sahraee Ardakan, Sundeep Rangan, Philip Schniter, Alyson K. Fletcher

In the two-layer neural-network learning problem, this scaling corresponds to the case where the number of input features, as well as training samples, grow to infinity but the number of hidden nodes stays fixed.

Imputation

Input-Output Equivalence of Unitary and Contractive RNNs

1 code implementation • NeurIPS 2019 • Melikasadat Emami, Mojtaba Sahraee Ardakan, Sundeep Rangan, Alyson K. Fletcher

Unitary recurrent neural networks (URNNs) have been proposed as a method to overcome the vanishing and exploding gradient problem in modeling data with long-term dependencies.