32 citations · 80 across the 12 of their papers we have counts for
12 papers
Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration
Athmanarayanan Lakshmi Narayanan, Amrutha Machireddy, Ranganath Krishnan
Active Learning (AL) has emerged as a powerful approach for minimizing labeling costs by selectively sampling the most informative data for neural network model development. Effect…
Parameter-Efficient Active Learning for Foundational models
Athmanarayanan Lakshmi Narayanan, Ranganath Krishnan, Amrutha Machireddy +1
Foundational vision transformer models have shown impressive few shot performance on many vision tasks. This research presents a novel investigation into the application of paramet…
HEAL: Brain-inspired Hyperdimensional Efficient Active Learning
Yang Ni, Zhuowen Zou, Wenjun Huang +6
Drawing inspiration from the outstanding learning capability of our human brains, Hyperdimensional Computing (HDC) emerges as a novel computing paradigm, and it leverages high-dime…
Reliable Multimodal Trajectory Prediction via Error Aligned Uncertainty Optimization
Neslihan Kose, Ranganath Krishnan, Akash Dhamasia +2
Reliable uncertainty quantification in deep neural networks is very crucial in safety-critical applications such as automated driving for trustworthy and informed decision-making.…
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…
Robust Contrastive Active Learning with Feature-guided Query Strategies
Ranganath Krishnan, Nilesh Ahuja, Alok Sinha +3
We introduce supervised contrastive active learning (SCAL) and propose efficient query strategies in active learning based on the feature similarity (featuresim) and principal comp…
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…
Uncertainty aware audiovisual activity recognition using deep Bayesian variational inference
Mahesh Subedar, Ranganath Krishnan, Paulo Lopez Meyer +2
Deep neural networks (DNNs) provide state-of-the-art results for a multitude of applications, but the approaches using DNNs for multimodal audiovisual applications do not consider…