Data vs. Model
The discussion emphasizes the critical role of data over model architecture in machine learning. Sara argues that while reinforcement learning offers one approach, the true advancement lies in refining how we sample data to better represent the underlying distribution. As we improve our sampling techniques, we can enhance model efficiency and achieve more effective training outcomes.In this clip
From this podcast
Machine Learning Street Talk (MLST)
#92 - SARA HOOKER - Fairness, Interpretability, Language Models
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