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Towards Representation Learning with Tractable Probabilistic Models., , and . CoRR, (2016)SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks., , , , , , , and . CoRR, (2019)Learning Accurate Cutset Networks by Exploiting Decomposability., , and . AI*IA, volume 9336 of Lecture Notes in Computer Science, page 221-232. Springer, (2015)Strudel: A fast and accurate learner of structured-decomposable probabilistic circuits., , and . Int. J. Approx. Reason., (2022)Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs., and . CoRR, (2022)Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification., , and . AAAI, page 12208-12216. AAAI Press, (2024)An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games., , , , , , and . EACL, page 2135-2144. Association for Computational Linguistics, (2021)Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations., , , , and . NeurIPS, (2020)Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures., , , , , , and . CoRR, (2019)Learning Bayesian Random Cutset Forests., , and . ISMIS, volume 9384 of Lecture Notes in Computer Science, page 122-132. Springer, (2015)