4 papers
Learning the Graph and the Embedding Together: Classifier-Independent Rewiring for Heterophilic Node Classification
Harshit Kumar, Sujan Chakraborty, Priyanka Saha +2
Graph neural networks lose much of their advantage on heterophilic graphs, where connected nodes often carry different labels. Graph rewiring is a popular remedy, but rewiring meth…
Towards Modality-Agnostic Medical Image Anomaly Detection: A Training-Free Manifold Refinement Approach
Pritam Kar, Gouri Lakshmi S, Saptarshi Bej
Deploying AI-based anomaly detection across diverse clinical imaging settings remains challenging because most existing methods rely on modality-specific architectures, anatomical…
Anomaly Detection via Mean Shift Density Enhancement
Pritam Kar, Rahul Bordoloi, Olaf Wolkenhauer +1
Unsupervised anomaly detection stands as an important problem in machine learning. Existing unsupervised anomaly detection algorithms rarely perform well across different anomaly t…
Spike Agreement Dependent Plasticity: A scalable Bio-Inspired learning paradigm for Spiking Neural Networks
Saptarshi Bej, Muhammed Sahad E, Gouri Lakshmi +3
We introduce Spike Agreement Dependent Plasticity (SADP), a biologically inspired synaptic learning rule for Spiking Neural Networks (SNNs) that relies on the agreement between pre…