27 citations · 65 across the 10 of their papers we have counts for
11 papers
Correcting Model Misspecification via Generative Adversarial Networks
Pronoma Banerjee, Manasi V Gude, Rajvi J Sampat +3
Machine learning models are often misspecified in the likelihood, which leads to a lack of robustness in the predictions. In this paper, we introduce a framework for correcting lik…
QuantProb: Generalizing Probabilities along with Predictions for a Pre-trained Classifier
Aditya Challa, Snehanshu Saha, Soma Dhavala
Quantification of Uncertainty in predictions is a challenging problem. In the classification settings, although deep learning based models generalize well, class probabilities ofte…
Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data
Snehanshu Saha, Jyotirmoy Sarkar, Soma Dhavala +2
Anomalies refer to the departure of systems and devices from their normal behaviour in standard operating conditions. An anomaly in an industrial device can indicate an upcoming fa…
LogGENE: A smooth alternative to check loss for Deep Healthcare Inference Tasks
Aryaman Jeendgar, Tanmay Devale, Soma S Dhavala +1
Mining large datasets and obtaining calibrated predictions from tem is of immediate relevance and utility in reliable deep learning. In our work, we develop methods for Deep neural…
Hamiltonian Monte Carlo Particle Swarm Optimizer
Omatharv Bharat Vaidya, Rithvik Terence DSouza, Snehanshu Saha +2
We introduce the Hamiltonian Monte Carlo Particle Swarm Optimizer (HMC-PSO), an optimization algorithm that reaps the benefits of both Exponentially Averaged Momentum PSO and HMC s…
Estimation and Applications of Quantiles in Deep Binary Classification
Anuj Tambwekar, Anirudh Maiya, Soma Dhavala +1
Quantile regression, based on check loss, is a widely used inferential paradigm in Econometrics and Statistics. The conditional quantiles provide a robust alternative to classical…
A framework for predicting, interpreting, and improving Learning Outcomes
Chintan Donda, Sayan Dasgupta, Soma S Dhavala +2
It has long been recognized that academic success is a result of both cognitive and non-cognitive dimensions acting together. Consequently, any intelligent learning platform design…
AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence
Rohan Mohapatra, Snehanshu Saha, Carlos A. Coello Coello +3
This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks. In order to support…
LALR: Theoretical and Experimental validation of Lipschitz Adaptive Learning Rate in Regression and Neural Networks
Snehanshu Saha, Tejas Prashanth, Suraj Aralihalli +3
We propose a theoretical framework for an adaptive learning rate policy for the Mean Absolute Error loss function and Quantile loss function and evaluate its effectiveness for regr…
A Framework for Democratizing AI
Shakkeel Ahmed, Ravi S. Mula, Soma S. Dhavala
Machine Learning and Artificial Intelligence are considered an integral part of the Fourth Industrial Revolution. Their impact, and far-reaching consequences, while acknowledged, a…
Bayesian object classification of gold nanoparticles
Bledar A. Konomi, Soma S. Dhavala, Jianhua Z. Huang +5
The properties of materials synthesized with nanoparticles (NPs) are highly correlated to the sizes and shapes of the nanoparticles. The transmission electron microscopy (TEM) imag…