55 citations · 74 across the 13 of their papers we have counts for
14 papers
CLIMAX: An exploration of Classifier-Based Contrastive Explanations
Praharsh Nanavati, Ranjitha Prasad
Explainable AI is an evolving area that deals with understanding the decision making of machine learning models so that these models are more transparent, accountable, and understa…
Over-The-Air Clustered Wireless Federated Learning
Ayush Madhan-Sohini, Divin Dominic, Nazreen Shah +1
Privacy and bandwidth constraints have led to the use of federated learning (FL) in wireless systems, where training a machine learning (ML) model is accomplished collaboratively w…
DAGSurv: Directed Acyclic Graph Based Survival Analysis Using Deep Neural Networks
Ansh Kumar Sharma, Rahul Kukreja, Ranjitha Prasad +1
Causal structures for observational survival data provide crucial information regarding the relationships between covariates and time-to-event. We derive motivation from the inform…
Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc Explainability
Aditya Saini, Ranjitha Prasad
Albeit the tremendous performance improvements in designing complex artificial intelligence (AI) systems in data-intensive domains, the black-box nature of these systems leads to t…
Path-Aware OMP Algorithms for Provenance Recovery in Wireless Networks
Shilpi Mishra, J. Harshan, Ranjitha Prasad
Low-latency provenance embedding methods have received traction in vehicular networks for their ability to track the footprint of information flow. One such known method is based o…
B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-Learning
Anish Madan, Ranjitha Prasad
There is a growing interest in the learning-to-learn paradigm, also known as meta-learning, where models infer on new tasks using a few training examples. Recently, meta-learning b…