lossless compression with probabilistic circuits

The algorithm adopts an CPU-based entropy coder rANS. 3.2). We note that both ideas can be integrated naturally: the simple latent distributions used by existing neural compression algorithms can be replaced by expressive PC models. A. Gritsenko, M. Dehghani, C. K. Snderby, and T. Salimans (2020), Idf++: analyzing and improving integer discrete flows for lossless compression, H. Xiao, K. Rasul, and R. Vollgraf (2017), Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, An introduction to neural data compression, M. Zhang, A. Zhang, and S. McDonagh (2021a), On the out-of-distribution generalization of probabilistic image modelling, S. Zhang, C. Zhang, N. Kang, and Z. Li (2021b), iVPF: numerical invertible volume preserving flow for efficient lossless compression, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Disentangling latent space for vae by label relevant/irrelevant dimensions, the~{}top-down~{}probability~{}of~{}every~{}% B.5. On the other hand, models that make strong independence assumptions (\egn-gram, fully-factorized) are cheap to evaluate but lack the expressiveness to model complex distributions over structured data such as images.222 Bringing together expressive PCs (\ieHCLTs) and our (de)compressor, we proceed to evaluate the compression performance of the proposed PC-based algorithm. This section provides additional details about the algorithm used to compute the conditional probabilities F(\x) (\ieAlg. Introduction to Laplace . Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. First, we assign probabilities to every input unit: for an input unit n defined on variable X, if evidence is provided for X in the query (\egX=x or X

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