3 papers
cs.LG2022
Explicitising The Implicit Intrepretability of Deep Neural Networks Via Duality
Chandrashekar Lakshminarayanan, Amit Vikram Singh, Arun Rajkumar
Recent work by Lakshminarayanan and Singh [2020] provided a dual view for fully connected deep neural networks (DNNs) with rectified linear units (ReLU). It was shown that (i) the…
cs.LG2021
Disentangling deep neural networks with rectified linear units using duality
Chandrashekar Lakshminarayanan, Amit Vikram Singh
Despite their success deep neural networks (DNNs) are still largely considered as black boxes. The main issue is that the linear and non-linear operations are entangled in every la…
cs.LG2020
Deep Gated Networks: A framework to understand training and generalisation in deep learning
Chandrashekar Lakshminarayanan, Amit Vikram Singh
Understanding the role of (stochastic) gradient descent (SGD) in the training and generalisation of deep neural networks (DNNs) with ReLU activation has been the object study in th…