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Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology
The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications, as the risk of model degradation in a changing environment is eminent.
Compact and Computationally Efficient Representations of Deep Neural Networks
However, most of these common matrix storage formats make strong statistical assumptions about the distribution of the elements in the matrix, and can therefore not efficiently represent the entire set of matrices that exhibit low entropy statistics (thus, the entire set of compressed neural network weight matrices).
Sharing Hash Codes for Multiple Purposes
In this paper, we propose {multiple purpose LSH (mp-LSH) which shares the hash codes for different dissimilarities.
How to Explain Individual Classification Decisions
After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data.
