linear costs in D and |p|, making the approach highly scalable. Empirically, our
PC-based (de)compression algorithm runs 5-40 times faster than neural compres-
sion algorithms that achieve similar bitrates. By scaling up the traditional PC
structure learning pipeline, we achieve state-of-the-art results on image datasets
such as MNIST. Furthermore, PCs can be naturally integrated with existing neural
compression algorithms to improve the performance of these base models on nat-
ural image datasets. Our results highlight the potential impact that non-standard
learning architectures may have on neural data compression.
LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS