3 papers
cs.LG2026
Extremely Fast and Compact Binary Graph Representations via Randomized Operator Sketching
Srajan Agarwal, Megha P, Bikas C Das +2
Graph neural networks typically rely on dense, floating-point node representations, which can impose substantial memory and computational costs. Binary graph hashing offers an alte…
cs.NE2026
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…
cs.NE2025
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…