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BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts.

, , , , , и . CoRR, (2024)

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Antonio Dell'Aquila

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