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Probabilistic Circuits (PCs) are a unified framework for tractable probabilistic models that support efficient computation of various probabilistic queries (e.g., marginal probabilities).
Least squares quantization in pcm
Stuart Lloyd · 1982
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An introduction to hidden markov models
Lawrence Rabiner and Biinghwang Juang · 1986
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Learning with mixtures of trees
Marina Meila and Michael I Jordan · 2000
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A differential approach to inference in bayesian networks
Adnan Darwiche · 2003
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And/or branch-and-bound for graphical models
Radu Marinescu and Rina Dechter · 2005
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
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Sum-product networks: A new deep architecture
Hoifung Poon and Pedro Domingos · 2011
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Learning the structure of sum-product networks
Robert Gens and Domingos Pedro · 2013
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Probabilistic sentential decision diagrams
Doga Kisa, Guy Van den Broeck, Arthur Choi, and Adnan Darwiche · 2014
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Cutset networks: A simple, tractable, and scalable approach for improving the accuracy of chow-liu trees
Tahrima Rahman, Prasanna Kothalkar, and Vibhav Gogate · 2014
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Learning the structure of sum-product networks via an svd-based algorithm
Tameem Adel, David Balduzzi, and Ali Ghodsi · 2015
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Pointer sentinel mixture models
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On the latent variable interpretation in sum-product networks
Robert Peharz, Robert Gens, Franz Pernkopf, and Pedro Domingos · 2016
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Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
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Glow: generative flow with invertible 1 × \times 1 convolutions
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BERT: pre-training of deep bidirectional transformers for language understanding
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Probabilistic circuits for variational inference in discrete graphical models
Andy Shih and Stefano Ermon · 2020
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Group fairness by probabilistic modeling with latent fair decisions
YooJung Choi, Meihua Dang, and Guy Van den Broeck · 2021
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Juice: A julia package for logic and probabilistic circuits
Meihua Dang, Pasha Khosravi, Yitao Liang, Antonio Vergari, and Guy Van den Broeck · 2021
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Random probabilistic circuits
Nicola Di Mauro, Gennaro Gala, Marco Iannotta, and Teresa MA Basile · 2021
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Tractable regularization of probabilistic circuits
Anji Liu and Guy Van den Broeck · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Spflow: An easy and extensible library for deep probabilistic learning using sum-product networks
Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Pranav Subramani, Nicola Di Mauro, Pascal Poupart, and Kristian Kersting · 2019
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Language models are unsupervised multitask learners
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Scaling hidden Markov language models
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Probabilistic circuits: A unifying framework for tractable probabilistic models
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An image is worth 16x16 words: Transformers for image recognition at scale
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Hyperspns: compact and expressive probabilistic circuits
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Continuous mixtures of tractable probabilistic models
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Sparse probabilistic circuits via pruning and growing
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Masked autoencoders are scalable vision learners
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Lossless compression with probabilistic circuits
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Conditional sum-product networks: Modular probabilistic circuits via gate functions
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