Smoothing Structured Decomposable Circuits
Authors: Andy Shih, Guy Van den Broeck, Paul Beame, Antoine Amarilli
NeurIPS 2019 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our final contribution (Section 8) is to experiment on smoothing and probabilistic inference tasks. We evaluate the performance of our smoothing and of our linear time All-Marginals algorithm. |
| Researcher Affiliation | Academia | Andy Shih University of California, Los Angeles EMAIL Guy Van den Broeck University of California, Los Angeles EMAIL Paul Beame University of Washington EMAIL Antoine Amarilli LTCI, Télécom Paris, IP Paris EMAIL |
| Pseudocode | Yes | Algorithm 1 all-marginals(g, w) |
| Open Source Code | Yes | The code for our experiments can be found at https://github.com/Andy Shih12/SSDC. |
| Open Datasets | Yes | In Table 2b we report the results on the Segmentation-11 network, which is a network from the 2006-2014 UAI Probabilistic Inference competitions. This particular network is a factor graph that was used to do image segmentation/classification (figure out what type of object each pixel corresponds to) [Forouzan, 2015]. |
| Dataset Splits | No | The paper does not specify dataset splits such as train/validation/test percentages, absolute sample counts, or describe a cross-validation setup for its experiments. |
| Hardware Specification | Yes | Experiments were run on a single Intel(R) Core(TM) i7-3770 CPU with 16GB of RAM. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | No | The paper describes the general setup of hand-crafted circuits and the context of collapsed sampling, but it does not provide specific experimental setup details such as hyperparameter values (e.g., learning rates, batch sizes) or optimizer settings. |