5 citations · 8 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…
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.…