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#92 - SARA HOOKER - Fairness, Interpretability, Language Models

Machine Learning Street Talk (MLST)

CHAPTER

Understanding Neural Networks Through Spline Theory and Model Adaptation

This chapter explores the crucial roles of model-based and white box approaches in neural networks, using spline theory to enhance interpretability. It also examines how multi-layer perceptrons define input space and decision boundaries, while addressing model scalability and resource adaptation for improved performance.

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