10 papers
EMMA: Extracting Multiple physical parameters from Multimodal Data
Farhat Shaikh, Ayan Banerjee, Sandeep Gupta
We introduce EMMA, a physics-informed multimodal framework that recovers all identifiable dynamical parameters of a system directly from raw video, audio, and image-based time-seri…
Human Knowledge Integrated Multi-modal Learning for Single Source Domain Generalization
Ayan Banerjee, Kuntal Thakur, Sandeep Gupta
Generalizing image classification across domains remains challenging in critical tasks such as fundus image-based diabetic retinopathy (DR) grading and resting-state fMRI seizure o…
Hardware Acceleration for Neural Networks: A Comprehensive Survey
Bin Xu, Ayan Banerjee, Sandeep Gupta
Neural networks have become dominant computational workloads across cloud and edge platforms, but their rapid growth in model size and deployment diversity has exposed hardware bot…
XAI-MeD: Explainable Knowledge Guided Neuro-Symbolic Framework for Domain Generalization and Rare Class Detection in Medical Imaging
Midhat Urooj, Ayan Banerjee, Sandeep Gupta
Explainability domain generalization and rare class reliability are critical challenges in medical AI where deep models often fail under real world distribution shifts and exhibit…
Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics
Bin Xu, Ayan Banerjee, Sandeep Gupta
Physical AI at the edge -- enabling autonomous systems to understand and predict real-world dynamics in real time -- requires hardware-efficient learning and inference. Model recov…
NEURO-GUARD: Neuro-Symbolic Generalization and Unbiased Adaptive Routing for Diagnostics -- Explainable Medical AI
Midhat Urooj, Ayan Banerjee, Sandeep Gupta
Accurate yet interpretable image-based diagnosis remains a central challenge in medical AI, particularly in settings characterized by limited data, subtle visual cues, and high-sta…