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Why does correctly ranking S* above Sout still result in an error output?
Question: In one to three sentences, explain why the system can still output Sout as an error even when ScoreA correctly ranks S* above Sout.
Sample answer: Because beam search is only an approximate search algorithm, it isn't guaranteed to find the output that truly maximizes ScoreA(S). Even though the scoring function correctly rates S* higher, the search process can settle on the lower-scoring Sout instead.
Key points:
- Beam search is an approximate algorithm
- It is not guaranteed to find the true maximum of ScoreA(S)
- The scoring function itself is not at fault in this case
Rubric: Full credit: mentions that beam search/search algorithm is approximate and can fail to find the true maximizer despite correct scoring. Partial credit: vague reference to search failure without connecting it to approximation. No credit: blames the scoring function.
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References
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
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