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License: MIT License
MAX SPN
License: MIT License
Our experiment results should use the paired p-test.
Both ID-SPN and LearnSPN are actually learned by LeranSPN methods, so the data maybe have a huge bias such that the performance of many methods are not so good. So we need more data. If we cannot find other data easily, generating random data is not a so bad way.
We should transfer the inapproximability result from BN.
We can treat the discrete variables as continual variables and use the gradient descent method to get a local minimal. Finally, we round it to make discrete.
Local search cannot promise exact solutions but can use efficient derivative methods. Exact search can use heuristic to pruning and find exact solution but cannot use the pros of efficient derivatives. What happens if we can find a method to combine the pros of them.
If we want to get the exact solution, we need some heuristics to pruning efficiently.
We need an experiment to prove our heuristic and derivative help.
We think that we cannot sample random SPNs from the SPN space, so we try to get SPN from other papers or github repos.
Top-K MaxMax is equivalent to Top-K TreeMAP. All the internal nodes are considered as hidden variables. The next step is to implement it to check the performance.
Check the first SPN paper to make sure our derivative theorem is new.
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