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Semantic Probabilistic Layers for Neuro-Symbolic Learning.

, , , , and . NeSy, volume 3432 of CEUR Workshop Proceedings, page 413. CEUR-WS.org, (2023)

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Learning Bayesian Random Cutset Forests., , and . ISMIS, volume 9384 of Lecture Notes in Computer Science, page 122-132. Springer, (2015)Conditional sum-product networks: Modular probabilistic circuits via gate functions., , , , , , and . Int. J. Approx. Reason., (2022)Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures., , , , , , and . CoRR, (2019)Visualizing and understanding Sum-Product Networks., , and . Mach. Learn., 108 (4): 551-573 (2019)End-to-end Learning of Deep Spatio-temporal Representations for Satellite Image Time Series Classification., , , , and . DC@PKDD/ECML, volume 1972 of CEUR Workshop Proceedings, CEUR-WS.org, (2017)Probabilistic Integral Circuits., , , , and . CoRR, (2023)Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts., , , and . CoRR, (2023)From MNIST to ImageNet and Back: Benchmarking Continual Curriculum Learning., , , , , and . CoRR, (2023)Neural Concept Formation in Knowledge Graphs., , and . AKBC, (2021)Automatic Bayesian Density Analysis., , , , , and . AAAI, page 5207-5215. AAAI Press, (2019)