10 papers
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…
Fast and Featureless Node Representation Learning with Partial Pairwise Supervision
Sujan Chakraborty, Saptarshi Bej
We introduce Contrastive FUSE, a fast and unified framework for scalable node representation learning in graphs with partially available pairwise node labels and no available node…
Multivariate Time Series Classification of Fermi-Detected Gamma-Ray Transients Using Convolutional-Recurrent Neural Networks
Arpan Aryam John, Krushna Govind Shete, Shabnam Iyyani +1
Fermi Gamma-ray Space Telescope has detected a diverse range of gamma-ray transients since its launch in 2008. Over the years, Fermi has accumulated an extensive public archive of…
Supervised Spike Agreement Dependent Plasticity for Fast Local Learning in Spiking Neural Networks
Gouri Lakshmi S, Athira Chandrasekharan, Harshit Kumar +3
Spike-Timing-Dependent Plasticity (STDP) provides a biologically grounded learning rule for spiking neural networks (SNNs), but its reliance on precise spike timing and pairwise up…
FUSE: Fast Semi-Supervised Node Embedding Learning via Structural and Label-Aware Optimization
Sujan Chakraborty, Rahul Bordoloi, Anindya Sengupta +2
Graph-based learning is a cornerstone for analyzing structured data, with node classification as a central task. However, in many real-world graphs, nodes lack informative feature…