7 citations · 22 across the 14 of their papers we have counts for
5 papers · 1 filter
EigenTrack: Spectral Activation Feature Tracking for Hallucination and Out-of-Distribution Detection in LLMs and VLMs
Davide Ettori, Nastaran Darabi, Sina Tayebati +4
Large language models (LLMs) offer broad utility but remain prone to hallucination and out-of-distribution (OOD) errors. We propose EigenTrack, an interpretable real-time detector…
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…
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…