Sparse Probabilistic Circuits via Pruning and Growing
Authors: Meihua Dang, Anji Liu, Guy Van den Broeck
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, our learner achieves state-of-the-art likelihoods on MNIST-family image datasets and on Penn Tree Bank language data compared to other PC learners and less tractable deep generative models such as flow-based models and variational autoencoders (VAEs). |
| Researcher Affiliation | Academia | Meihua Dang CS Department UCLA mhdang@cs.ucla.edu Anji Liu CS Department UCLA liuanji@cs.ucla.edu Guy Van den Broeck CS Department UCLA guyvdb@cs.ucla.edu |
| Pseudocode | Yes | Algorithm 1: PC sampling |
| Open Source Code | Yes | Code and experiments are available at https://github.com/UCLA-Star AI/Sparse PC. |
| Open Datasets | Yes | We now evaluate our proposed method pruning and growing on two different sets of density estimation benchmarks: (1) the MNIST-family image generation datasets including MNIST [22], EMNIST [5], and Fashion MNIST [46]; (2) the character-level Penn Tree Bank language modeling task [27]. |
| Dataset Splits | Yes | We split out 5% of training data as a validation set. We perform early stopping and hyperparameter search using a validation set and report results on the test set. |
| Hardware Specification | No | The paper mentions "customized GPU kernels" but does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for the experiments. |
| Software Dependencies | No | The paper mentions "Py Torch" and "Juice.jl" but does not specify their version numbers. |
| Experiment Setup | Yes | We use hidden Chow-Liu Trees (HCLTs) [25] with the number of latent states in {16, 32, 64, 128} as initial PC structures. We train the parameters of PCs with stochastic mini-batch EM (cf. Section 5). We perform early stopping and hyperparameter search using a validation set and report results on the test set. Please refer to Appendix C for more details. |