20 citations · 20 across the 4 of their papers we have counts for
4 papers · 1 filter
Assessing Architectural Similarity in Populations of Deep Neural Networks
Audrey Chung, Paul Fieguth, Alexander Wong
Evolutionary deep intelligence has recently shown great promise for producing small, powerful deep neural network models via the synthesis of increasingly efficient architectures o…
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks
Xiaodan Hu, Audrey G. Chung, Paul Fieguth +3
Generative Adversarial Networks (GANs) have shown considerable promise for mitigating the challenge of data scarcity when building machine learning-driven analysis algorithms. Spec…
Mitigating Architectural Mismatch During the Evolutionary Synthesis of Deep Neural Networks
Audrey Chung, Paul Fieguth, Alexander Wong
Evolutionary deep intelligence has recently shown great promise for producing small, powerful deep neural network models via the organic synthesis of increasingly efficient archite…
A new take on measuring relative nutritional density: The feasibility of using a deep neural network to assess commercially-prepared pureed food concentrations
Kaylen J. Pfisterer, Robert Amelard, Audrey G. Chung +1
Dysphagia affects 590 million people worldwide and increases risk for malnutrition. Pureed food may reduce choking, however preparation differences impact nutrient density making q…