7 citations · 22 across the 14 of their papers we have counts for
14 papers
Synthetic Data Generation for Long-Tail Medical Image Classification: A Case Study in Skin Lesions
Jiaxiang Jiang, Mahesh Subedar, Omesh Tickoo
Long-tailed class distributions are pervasive in multi-class medical datasets and pose significant challenges for deep learning models which typically underperform on tail classes…
PEFA-AI: Advancing Open-source LLMs for RTL generation using Progressive Error Feedback Agentic-AI
Athma Narayanan, Mahesh Subedar, Omesh Tickoo
We present an agentic flow consisting of multiple agents that combine specialized LLMs and hardware simulation tools to collaboratively complete the complex task of Register Transf…
DOSA: Differentiable Model-Based One-Loop Search for DNN Accelerators
Charles Hong, Qijing Huang, Grace Dinh +2
In the hardware design space exploration process, it is critical to optimize both hardware parameters and algorithm-to-hardware mappings. Previous work has largely approached this…
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…
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…
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…
Partially-Supervised Novel Object Captioning Leveraging Context from Paired Data
Shashank Bujimalla, Mahesh Subedar, Omesh Tickoo
In this paper, we propose an approach to improve image captioning solution for images with novel objects that do not have caption labels in the training dataset. We refer to our ap…
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…
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…