[42] Charles Sutton - Efficient Training Methods for Conditional Random Fields
The Thesis Review
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From Probabilities to Deep Learning: The Evolution of AI Models
This chapter explores the evolution of artificial intelligence ideas, highlighting the transition from probabilistic methods like Conditional Random Fields (CRFs) to modern deep learning approaches. It discusses the significance of encoding common sense knowledge and the challenges faced in capturing long-distance dependencies in structured prediction tasks. The conversation also reflects on the complexities of memorization and generalization within machine learning models, emphasizing the need for a conceptual framework to understand their outputs effectively.
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