4 papers
Training-Free Cross-Architecture Merging for Graph Neural Networks
Rishabh Bhattacharya, Vikaskumar Kalsariya, Naresh Manwani
Model merging has emerged as a powerful paradigm for combining the capabilities of distinct expert models without the high computational cost of retraining, yet current methods are…
EdgeMask-DG*: Learning Domain-Invariant Graph Structures via Adversarial Edge Masking
Rishabh Bhattacharya, Naresh Manwani
Structural shifts pose a significant challenge for graph neural networks, as graph topology acts as a covariate that can vary across domains. Existing domain generalization methods…
Robust Object Detection with Pseudo Labels from VLMs using Per-Object Co-teaching
Uday Bhaskar, Rishabh Bhattacharya, Avinash Patel +3
Foundation models, especially vision-language models (VLMs), offer compelling zero-shot object detection for applications like autonomous driving, a domain where manual labelling i…
MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration
Rishabh Bhattacharya, Hari Shankar, Vaishnavi Shivkumar +1
The growing adoption of Graph Neural Networks (GNNs) in high-stakes domains like healthcare and finance demands reliable explanations of their decision-making processes. While inhe…