6 citations · 7 across the 3 of their papers we have counts for
7 papers
Mitigating Sampling Bias and Improving Robustness in Active Learning
Ranganath Krishnan, Alok Sinha, Nilesh Ahuja +3
This paper presents simple and efficient methods to mitigate sampling bias in active learning while achieving state-of-the-art accuracy and model robustness. We introduce supervise…
Data augmentation to improve robustness of image captioning solutions
Shashank Bujimalla, Mahesh Subedar, Omesh Tickoo
In this paper, we study the impact of motion blur, a common quality flaw in real world images, on a state-of-the-art two-stage image captioning solution, and notice a degradation i…
B-SCST: Bayesian Self-Critical Sequence Training for Image Captioning
Shashank Bujimalla, Mahesh Subedar, Omesh Tickoo
Bayesian deep neural networks (DNNs) can provide a mathematically grounded framework to quantify uncertainty in predictions from image captioning models. We propose a Bayesian vari…
Deep Probabilistic Models to Detect Data Poisoning Attacks
Mahesh Subedar, Nilesh Ahuja, Ranganath Krishnan +2
Data poisoning attacks compromise the integrity of machine-learning models by introducing malicious training samples to influence the results during test time. In this work, we inv…
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes
Ranganath Krishnan, Mahesh Subedar, Omesh Tickoo
Stochastic variational inference for Bayesian deep neural network (DNN) requires specifying priors and approximate posterior distributions over neural network weights. Specifying m…
BAR: Bayesian Activity Recognition using variational inference
Ranganath Krishnan, Mahesh Subedar, Omesh Tickoo
Uncertainty estimation in deep neural networks is essential for designing reliable and robust AI systems. Applications such as video surveillance for identifying suspicious activit…