Interpretable Explanations for Probabilistic. Inference in Markov Logic. Khan Mohammad Al Farabi. University of Memphis kfarabi@memphis.edu. Somdeb Sarkhel.

In this paper, we develop an approach to explain the results of probabilistic inference in MLNs. Unlike approaches such as LIME and SHAP that explain black-box ...

In this paper, we develop an approach to explain the results of probabilistic inference in MLNs. Unlike approaches such as LIME and SHAP that explain black-box ...

We examine the problem of filtering for dynamic probabilistic systems using Markov Logic Networks. We propose a method to approximately compute the marginal ...

Interpretable Explanations for Probabilistic Inference in Markov Logic · Khan ... 2021. TLDR. This paper develops an approach to explain the results of ...

Venugopal, "Interpretable Explanations for Probabilistic Inference in Markov Logic", InProceedings, International Conference on Big Data, December 2021, 201 ...

Interpretable Explanations for Probabilistic Inference in Markov Logicmore. by Khan Mohammad Al Farabi. Markov Logic Networks (MLNs) represent relational ...

New paper on identifying math learning strategies at AI4ED@AAAI 2023. New paper on verifying visual captioning with probabilistic theorem proving in Markov ...

In this dissertation, we have developed a suite of fundamental techniques that help us in i) explaining probabilistic inference in MLNs and also ii) utilize ...

In this paper, we develop an approach to explain the results of probabilistic inference in MLNs. Unlike approaches such as LIME and SHAP that explain black-box ...