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Found 2 papers, 1 papers with code
Quantifying Explainability in NLP and Analyzing Algorithms for Performance-Explainability Tradeoff
The healthcare domain is one of the most exciting application areas for machine learning, but a lack of model transparency contributes to a lag in adoption within the industry.
Theoretical and Empirical Analysis of a Parallel Boosting Algorithm
In this paper we discuss a meta-learning algorithm (PSBML) which combines features of parallel algorithms with concepts from ensemble and boosting methodologies to achieve the desired scalability property.
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