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How Machines Learn to Ignore the Noise (Kevin Ellis + Zenna Tavares)

Machine Learning Street Talk (MLST)

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Automated Epistemic Foraging in Machine Learning

This chapter delves into the automation of epistemic foraging within machine learning, addressing the blend of classic algorithms and learned systems. It discusses the importance of inductive biases and the historical context of Bayesian models while exploring the role of abstractions in enhancing machine learning. The conversation also covers iterative problem-solving techniques and the philosophical differences in model design, emphasizing the potential of hybrid approaches between neural networks and traditional programming.

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