RASP Framework Insights
RASP serves as a framework for creating programs that can be transformed into transformer models, allowing for easier interpretation of their functions. The goal is to train transformers that can autonomously derive solutions and convert them into a more interpretable program representation. By imposing constraints on transformer weights, the approach ensures a direct mapping to RASP programs, emphasizing a discrete optimization problem within this innovative paradigm.In this clip
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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Learning Transformer Programs with Dan Friedman - 667
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