6 citations · 10 across the 4 of their papers we have counts for
5 papers
A Deeper Look at the Hessian Eigenspectrum of Deep Neural Networks and its Applications to Regularization
Adepu Ravi Sankar, Yash Khasbage, Rahul Vigneswaran +1
Loss landscape analysis is extremely useful for a deeper understanding of the generalization ability of deep neural network models. In this work, we propose a layerwise loss landsc…
DANTE: Deep AlterNations for Training nEural networks
Vaibhav B Sinha, Sneha Kudugunta, Adepu Ravi Sankar +3
We present DANTE, a novel method for training neural networks using the alternating minimization principle. DANTE provides an alternate perspective to traditional gradient-based ba…
On the Analysis of Trajectories of Gradient Descent in the Optimization of Deep Neural Networks
Adepu Ravi Sankar, Vishwak Srinivasan, Vineeth N Balasubramanian
Theoretical analysis of the error landscape of deep neural networks has garnered significant interest in recent years. In this work, we theoretically study the importance of noise…
ADINE: An Adaptive Momentum Method for Stochastic Gradient Descent
Vishwak Srinivasan, Adepu Ravi Sankar, Vineeth N Balasubramanian
Two major momentum-based techniques that have achieved tremendous success in optimization are Polyak's heavy ball method and Nesterov's accelerated gradient. A crucial step in all…
Are Saddles Good Enough for Deep Learning?
Adepu Ravi Sankar, Vineeth N Balasubramanian
Recent years have seen a growing interest in understanding deep neural networks from an optimization perspective. It is understood now that converging to low-cost local minima is s…