5 papers · 1 filter
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks
Neel Mishra, Bamdev Mishra, Pratik Jawanpuria +1
A novel first-order method is proposed for training generative adversarial networks (GANs). It modifies the Gauss-Newton method to approximate the min-max Hessian and uses the Sher…
Alpha Elimination: Using Deep Reinforcement Learning to Reduce Fill-In during Sparse Matrix Decomposition
Arpan Dasgupta, Pawan Kumar
A large number of computational and scientific methods commonly require decomposing a sparse matrix into triangular factors as LU decomposition. A common problem faced during this…
Light-weight Deep Extreme Multilabel Classification
Istasis Mishra, Arpan Dasgupta, Pratik Jawanpuria +2
Extreme multi-label (XML) classification refers to the task of supervised multi-label learning that involves a large number of labels. Hence, scalability of the classifier with inc…
Angle based dynamic learning rate for gradient descent
Neel Mishra, Pawan Kumar
In our work, we propose a novel yet simple approach to obtain an adaptive learning rate for gradient-based descent methods on classification tasks. Instead of the traditional appro…
Adaptive Consensus Optimization Method for GANs
Sachin Kumar Danisetty, Santhosh Reddy Mylaram, Pawan Kumar
We propose a second order gradient based method with ADAM and RMSprop for the training of generative adversarial networks. The proposed method is fastest to obtain similar accuracy…