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In machine translation, there is a widely used BLEU score ( https://en.wikipedia.org/wiki/BLEU ). It simply counts the matching n-grams between two segments of text and returns a 0-1 score based on that. The problem with this method is that it would give the same score to pairs "It is hot"/"It is cold" and "It is hot" / "It ...


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Surely you can implement such algorithm, since you already know the details. Iterative steps: Determine possible numbers in all empty squares Find the square with the least number of possible numbers Does this square have an unique solution? If yes, set it and GOTO 1 Else apply the backtracking logic and guess a number, GOTO 1 People who are good with ...


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Mixture of Experts might be what you are looking for. A Mixture of Experts model (MoE), divides a task into subtasks and designs seperate models for each of the tasks (This would be N in your case). It also defines a gating model to decide which expert to use, and during inference it uses the gating model output to pool/select predictions and makes the final ...


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