8 citations · 10 across the 7 of their papers we have counts for
8 papers
A Granger-Causal Perspective on Gradient Descent with Application to Pruning
Aditya Shah, Aditya Challa, Sravan Danda +2
Stochastic Gradient Descent (SGD) is the main approach to optimizing neural networks. Several generalization properties of deep networks, such as convergence to a flatter minima, a…
A novel RNA pseudouridine site prediction model using Utility Kernel and data-driven parameters
Sourabh Patil, Archana Mathur, Raviprasad Aduri +1
RNA protein Interactions (RPIs) play an important role in biological systems. Recently, we have enumerated the RPIs at the residue level and have elucidated the minimum structural…
DeliverAI: Reinforcement Learning Based Distributed Path-Sharing Network for Food Deliveries
Ashman Mehra, Snehanshu Saha, Vaskar Raychoudhury +1
Delivery of items from the producer to the consumer has experienced significant growth over the past decade and has been greatly fueled by the recent pandemic. Amazon Fresh, Shopif…
To prune or not to prune : A chaos-causality approach to principled pruning of dense neural networks
Rajan Sahu, Shivam Chadha, Nithin Nagaraj +2
Reducing the size of a neural network (pruning) by removing weights without impacting its performance is an important problem for resource-constrained devices. In the past, pruning…
Quantifying the Classification of Exoplanets: in Search for the Right Habitability Metric
Margarita Safonova, Archana Mathur, Suryoday Basak +2
What is habitability? Can we quantify it? What do we mean under the term habitable or potentially habitable planet? With estimates of the number of planets in our Galaxy alone runn…
SBAF: A New Activation Function for Artificial Neural Net based Habitability Classification
Snehanshu Saha, Archana Mathur, Kakoli Bora +2
We explore the efficacy of using a novel activation function in Artificial Neural Networks (ANN) in characterizing exoplanets into different classes. We call this Saha-Bora Activat…