Probabilistic Generating Circuits
Authors: Honghua Zhang, Brendan Juba, Guy Van Den Broeck
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section evaluates PGCs ability to model real data on density estimation benchmarks. |
| Researcher Affiliation | Academia | 1Computer Science Department, University of California Los Angeles, USA 2Computer Science Department, Washington University in St. Louis, Missouri, USA. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We evaluate PGCs on two density estimation benchmarks: 1. Twenty Datasets (Van Haaren & Davis, 2012)... 2. Amazon Baby Registries... This benchmark has been commonly used to evaluate DPP learners (Gillenwater et al., 2014; Mariet & Sra, 2015; Gartrell et al., 2019). |
| Dataset Splits | Yes | We randomly split each dataset into train (70%), valid (10%) and test (20%) sets. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., specific GPU or CPU models) used for running the experiments. |
| Software Dependencies | No | We implement Simple PGC in Py Torch and learn the parameters by maximum likelihood estimation (MLE). In particular, we use Adam (Kingma & Ba, 2014) as the optimizing algorithm to minimize the negative log likelihoods given the training sets. No specific versions are mentioned for PyTorch or Adam. |
| Experiment Setup | Yes | We use a weighted sum over Det PGCs as our model. [...] The structure of a Simple PGC is also governed by two hyperparameters: the number of Det PGCs in the weighted sum (denoted by C) and the maximum number of variables (i.e. k in Figure 2) allowed in each group (denoted by K). We tune C and K by a grid search over the following ranges: K {1, 2, 5, 7} and C {1, 4, 7, 10, 20}. and We implement Simple PGC in Py Torch and learn the parameters by maximum likelihood estimation (MLE). In particular, we use Adam (Kingma & Ba, 2014) as the optimizing algorithm to minimize the negative log likelihoods given the training sets. Regularization is done by setting the weight decay parameter in Adam. |