Search Results for author: Stone Yun

Found 6 papers, 1 papers with code

GHN-QAT: Training Graph Hypernetworks to Predict Quantization-Robust Parameters of Unseen Limited Precision Neural Networks

no code implementations24 Sep 2023 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.

Quantization

GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks

no code implementations26 Aug 2022 Stone Yun, Alexander Wong

We conduct the first-ever study exploring the use of graph hypernetworks for predicting parameters of unseen quantized CNN architectures.

Adversarial Robustness Parameter Prediction +1

Reproducing BowNet: Learning Representations by Predicting Bags of Visual Words

1 code implementation10 Jan 2022 Harry Nguyen, Stone Yun, Hisham Mohammad

In the paper, the author describes BowNet as a network consisting of a convolutional feature extractor $\Phi(\cdot)$ and a Dense-softmax layer $\Omega(\cdot)$ trained to predict BoW features from images.

Self-Supervised Learning

FactorizeNet: Progressive Depth Factorization for Efficient Network Architecture Exploration Under Quantization Constraints

no code implementations30 Nov 2020 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-power inference on the edge.

Quantization

Where Should We Begin? A Low-Level Exploration of Weight Initialization Impact on Quantized Behaviour of Deep Neural Networks

no code implementations30 Nov 2020 Stone Yun, Alexander Wong

The fine-grained, layerwise analysis enables us to gain deep insights on how initial weights distributions will affect final accuracy and quantized behaviour.

Quantization

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