most citedQuantifying How Training Gradient Sparsity Affect Spiking Neural Network Accuracy And Robustness

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.NE2025

Parameter efficient hybrid spiking-quantum convolutional neural network with surrogate gradient and quantum data-reupload

Luu Trong Nhan, Luu Trung Duong, Pham Ngoc Nam +1

The rapid advancement of artificial intelligence (AI) and deep learning (DL) has catalyzed the emergence of several optimization-driven subfields, notably neuromorphic computing an…

cs.NE2025

Hybrid Temporal-8-Bit Spike Coding for Spiking Neural Network Surrogate Training

Luu Trong Nhan, Luu Trung Duong, Pham Ngoc Nam +1

Spiking neural networks (SNNs) have emerged as a promising direction in both computational neuroscience and artificial intelligence, offering advantages such as strong biological p…

cs.NE2025★ 1 cited

Hybrid Layer-Wise ANN-SNN With Surrogate Spike Encoding-Decoding Structure

Nhan T. Luu, Duong T. Luu, Pham Ngoc Nam +1

Spiking Neural Networks (SNNs) have gained significant traction in both computational neuroscience and artificial intelligence for their potential in energy-efficient computing. In…

cs.NE2025★ 1 cited

Quantifying How Training Gradient Sparsity Affect Spiking Neural Network Accuracy And Robustness

Luu Trong Nhan, Nhan Trong Luu, Luu Trung Duong +3

Spiking Neural Networks (SNNs) have recently received increasing attention in both computational neuroscience and artificial intelligence owing to their potential for energy-effici…

cs.CV2024

CLIP Unreasonable Potential in Single-Shot Face Recognition

Nhan T. Luu

Face recognition is a core task in computer vision designed to identify and authenticate individuals by analyzing facial patterns and features. This field intersects with artificia…