DFS Sampling Insights
The discussion highlights the effectiveness of the DFS solution in generating high-probability outcomes while minimizing computational challenges. A trade-off was established between search depth and solution clarity, with varying success rates based on the percentage threshold set. Interestingly, switching to a larger model improved the pruning process, allowing for more efficient computations even within limited time constraints.In this clip
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Machine Learning Street Talk (MLST)
Daniel Franzen & Jan Disselhoff - ARC Prize 2024 winners
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