5 citations · 8 across the 8 of their papers we have counts for
8 papers
Starting Positions Matter: A Study on Better Weight Initialization for Neural Network Quantization
Stone Yun, Alexander Wong
Deep neural network (DNN) quantization for fast, efficient inference has been an important tool in limiting the cost of machine learning (ML) model inference. Quantization-specific…
GHN-QAT: Training Graph Hypernetworks to Predict Quantization-Robust Parameters of Unseen Limited Precision Neural Networks
Stone Yun, Alexander Wong
Graph Hypernetworks (GHN) can predict the parameters of varying unseen CNN architectures with surprisingly good accuracy at a fraction of the cost of iterative optimization. Follow…
GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks
Stone Yun, Alexander Wong
Deep convolutional neural network (CNN) training via iterative optimization has had incredible success in finding optimal parameters. However, modern CNN architectures often contai…
Reproducing BowNet: Learning Representations by Predicting Bags of Visual Words
Harry Nguyen, Stone Yun, Hisham Mohammad
This work aims to reproduce results from the CVPR 2020 paper by Gidaris et al. Self-supervised learning (SSL) is used to learn feature representations of an image using an unlabele…
Dream to Explore: Adaptive Simulations for Autonomous Systems
Zahra Sheikhbahaee, Dongshu Luo, Blake VanBerlo +3
One's ability to learn a generative model of the world without supervision depends on the extent to which one can construct abstract knowledge representations that generalize acros…
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
As the "Mobile AI" revolution continues to grow, so does the need to understand the behaviour of edge-deployed deep neural networks. In particular, MobileNets are the go-to family…
FactorizeNet: Progressive Depth Factorization for Efficient Network Architecture Exploration Under Quantization Constraints
Stone Yun, Alexander Wong
Depth factorization and quantization have emerged as two of the principal strategies for designing efficient deep convolutional neural network (CNN) architectures tailored for low-…
Where Should We Begin? A Low-Level Exploration of Weight Initialization Impact on Quantized Behaviour of Deep Neural Networks
Stone Yun, Alexander Wong
With the proliferation of deep convolutional neural network (CNN) algorithms for mobile processing, limited precision quantization has become an essential tool for CNN efficiency.…