32 citations · 38 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…
Improving model calibration with accuracy versus uncertainty optimization
Ranganath Krishnan, Omesh Tickoo
Obtaining reliable and accurate quantification of uncertainty estimates from deep neural networks is important in safety-critical applications. A well-calibrated model should be ac…
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Umang Bhatt, Javier Antorán, Yunfeng Zhang +12
Algorithmic transparency entails exposing system properties to various stakeholders for purposes that include understanding, improving, and contesting predictions. Until now, most…
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…