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deeceeo t1_j5wnrf1 wrote

How would you compare Tselin Machines to other intrinsically interpretable models, like the sparse decision trees that Cynthia Rudin's group works on? Both in terms of capacity/expressiveness and interpretability.

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olegranmo OP t1_j5xpnj2 wrote

Great question! Rudin et al.’s approach elegantly builds an optimal decision tree through search. TM learns online, processing one example at a time, like a neural network. Also, like logistic regression, TM adds up evidence from different features, however, it builds non-linear logical rules, instead of operating on single features. TM also supports convolution for image processing and time series. It can also learn from penalties and rewards addressing the contextual bandit problem. Finally, TMs allow self-supervised learning by means of an auto-encoder. So, quite different from decision trees.

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RedYican t1_j6ih1ui wrote

Does it make sense to combine Tsetlin Machine with NNs (language understanding) via triplets?

If we had some statements S_n about entity X and then some other statement as training example Sn+1 could one use TM to discover what other statements matter for Sn+1?

EDIT: found your other paper - https://arxiv.org/pdf/2102.10952.pdf

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RedYican t1_j6ij9j7 wrote

Follow up question: Could one use it on NN embeddings directly?

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RedYican t1_j6ijnh3 wrote

One more follow-up: Is there any try to combine this with Fuzzy Logic?

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