4 citations · 5 across the 5 of their papers we have counts for
4 papers · 1 filter
Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks
Cody Blakeney, Gentry Atkinson, Nathaniel Huish +3
Algorithmic bias is of increasing concern, both to the research community, and society at large. Bias in AI is more abstract and unintuitive than traditional forms of discriminatio…
Lipschitz Continuity Guided Knowledge Distillation
Yuzhang Shang, Bin Duan, Ziliang Zong +2
Knowledge distillation has become one of the most important model compression techniques by distilling knowledge from larger teacher networks to smaller student ones. Although grea…
Simon Says: Evaluating and Mitigating Bias in Pruned Neural Networks with Knowledge Distillation
Cody Blakeney, Nathaniel Huish, Yan Yan +1
In recent years the ubiquitous deployment of AI has posed great concerns in regards to algorithmic bias, discrimination, and fairness. Compared to traditional forms of bias or disc…
Parallel Blockwise Knowledge Distillation for Deep Neural Network Compression
Cody Blakeney, Xiaomin Li, Yan Yan +1
Deep neural networks (DNNs) have been extremely successful in solving many challenging AI tasks in natural language processing, speech recognition, and computer vision nowadays. Ho…