[12] Martha White - Regularized Factor Models
The Thesis Review
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Exploring Sparse Coding and Machine Learning Techniques
This chapter examines the intersections between classical machine learning methods and contemporary deep learning practices, particularly focusing on techniques like PCA and CCA. It highlights the importance of sparse coding in representation learning and reinforcement learning, discussing its role in addressing sparsity and knowledge retention in continual learning systems. The complexities of reinforcement learning, especially regarding bootstrapping and value estimation, are also explored, along with insights into brain connectomes and their implications for extracting meaningful data.
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