4 papers
HyperFuse: Fast Self-Supervised Node Embeddings for Attributed Hypergraphs
Megha P, Harshit Kumar, Srajan Agarwal +3
Self-supervised hypergraph representation learning can produce informative node embeddings, but existing methods often require deep encoders trained for hundreds of epochs, making…
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…
Building Supervision into Hebbian Plasticity through Spike Agreement
Gouri Lakshmi S, Athira Chandrasekharan, Harshit Kumar +3
Supervised learning in spiking neural networks (SNNs) typically requires either gradient-based backpropagation, which sacrifices the Hebbian, spike-driven character of biological p…
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…