6 citations · 6 across the 4 of their papers we have counts for
4 papers
SAGE: Surrogate-gradient Adaptation via Attention-Guided Entropy for Spiking Transformers
Kiran Nair, Rodrigue Rizk, KC Santosh
Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep neural networks by exploiting sparse event-driven computation, but their training remains…
Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI
Casey Wall, Longwei Wang, Rodrigue Rizk +1
Gradient-weighted Class Activation Mapping (Grad-CAM) is widely used to visualize model decisions, but it was originally formulated for convolutional neural networks, where spatial…
Uncertainty-Aware LLM-Guided Policy Shaping for Sparse-Reward Reinforcement Learning
Ujjwal Bhatta, Utsabi Dangol, Sumaly Bajracharya +2
Sparse rewards and heterogeneous task sequences remain persistent challenges in Reinforcement Learning (RL), often resulting in slow convergence, weak generalization, and inefficie…
PC-SNN: Predictive Coding-based Local Hebbian Plasticity Learning in Spiking Neural Networks
Haidong Wang, Xiaogang Xiong, Mengting Lan +5
Spiking Neural Networks (SNNs), regarded as the third generation of neural networks, emulate the brain's information processing with unparalleled biological plausibility compared t…