![]() ![]() Third, we reveal that model similarity has strong correlations with pruning induced bias, which provides a powerful method to explain why bias occurs in pruned neural networks. Easy Mode In Easy mode, the keyboard of the AI is a direct copy of your own, making it much easier to play along with the sequence of notes. Second, we demonstrate that knowledge distillation can mitigate induced bias in pruned neural networks, even with unbalanced datasets. First, we propose two simple yet effective metrics, Combined Error Variance (CEV) and Symmetric Distance Error (SDE), to quantitatively evaluate the induced bias prevention quality of pruned models. In this work, we strive to tackle the challenging issues of evaluating, mitigating, and explaining induced bias in pruned neural networks. Advice and guidance feeding in is coming from HRV. ![]() The position of the Founder & CEO is occupied by Shamir Allibhai. A clear gap exists in the current literature on evaluating and mitigating bias in pruned neural networks. Simon Says is the first Artificial Intelligence (AI) expert system for HRV training and recovery guidance. The company currently specializes in the Computer Software, Artificial Intelligence (AI) areas. Compared to traditional forms of bias or discrimination caused by humans, algorithmic bias generated by AI is more abstract and unintuitive therefore more difficult to explain and mitigate. In recent years the ubiquitous deployment of AI has posed great concerns in regards to algorithmic bias, discrimination, and fairness. ![]()
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