Where Should We Begin? A Low-Level Exploration of Weight Initialization Impact on Quantized Behaviour of Deep Neural Networks. - Stone Yun; Alexander Wong (CoRR, 2020)

FactorizeNet: Progressive Depth Factorization for Efficient Network Architecture Exploration Under Quantization Constraints. - Stone Yun; Alexander Wong (CoRR, 2020)

Do All MobileNets Quantize Poorly? Gaining Insights Into the Effect of Quantization on Depthwise Separable Convolutional Networks Through the Eyes of Multi-Scale Distributional Dynamics. - Stone Yun; Alexander Wong (CVPR Workshops, 2021)

Reproducing BowNet: Learning Representations by Predicting Bags of Visual Words. - Harry Nguyen; Stone Yun; Hisham Mohammad (CoRR, 2022)

GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks. - Stone Yun; Alexander Wong (CoRR, 2022)

GHN-QAT: Training Graph Hypernetworks to Predict Quantization-Robust Parameters of Unseen Limited Precision Neural Networks. - Stone Yun; Alexander Wong (CoRR, 2023)

Starting Positions Matter: A Study on Better Weight Initialization for Neural Network Quantization. - Stone Yun; Alexander Wong (CoRR, 2025)