30 citations · 136 across the 18 of their papers we have counts for
4 papers · 1 filter
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…
EdgeSpeechNets: Highly Efficient Deep Neural Networks for Speech Recognition on the Edge
Zhong Qiu Lin, Audrey G. Chung, Alexander Wong
Despite showing state-of-the-art performance, deep learning for speech recognition remains challenging to deploy in on-device edge scenarios such as mobile and other consumer devic…
Nature vs. Nurture: The Role of Environmental Resources in Evolutionary Deep Intelligence
Audrey G. Chung, Paul Fieguth, Alexander Wong
Evolutionary deep intelligence synthesizes highly efficient deep neural networks architectures over successive generations. Inspired by the nature versus nurture debate, we propose…