Variable Elimination in the Fourier Domain
Authors: Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla, Bart Selman
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the significance of this approach by applying it to the variable elimination algorithm. Compared with the traditional bucket representation and other approximate inference algorithms, we obtain significant improvements. [...] 5. Experiments |
| Researcher Affiliation | Academia | Yexiang Xue YEXIANG@CS.CORNELL.EDU Cornell University, Ithaca, NY, 14853, USA Stefano Ermon ERMON@CS.STANFORD.EDU Stanford University, Stanford, CA, 94305, USA Ronan Le Bras, Carla P. Gomes, Bart Selman {LEBRAS,GOMES,SELMAN}@CS.CORNELL.EDU Cornell University, Ithaca, NY, 14853, USA |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any link or explicit statement about the availability of its own source code. It mentions using Lib DAI and ACE, which are third-party tools. |
| Open Datasets | Yes | We compare our inference algorithms on large benchmarks from the UAI 2010 Approximate Inference Challenge (UAI). [...] Uai 2010 approximate inference challenge. http:// www.cs.huji.ac.il/project/UAI10. |
| Dataset Splits | No | The paper mentions using UAI 2010 challenge benchmarks and generating synthetic training images, but it does not specify any train/validation/test splits for these datasets or instances. It describes the total number of training images but not how they are partitioned. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Lib DAI (Mooij, 2010)" and "ACE (Darwiche & Marquis, 2002)" but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | For a fair comparison, we fix the size of the messages for both Fourier VE and Mini-bucket to 2^10 = 1, 024. [...] we set the message size for Fourier VE to be 1,048,576 (2^20). Because the complexity of the multiplication step in Fourier VE is quadratic in the number of coefficients, we further shrink the message size to 1,024 (2^10) during multiplication. We allow 1,000,000 steps for burn in and another 1,000,000 steps for sampling in the MCMC approach. [...] damping rate of 0.1 and the maximal number of iterations 1,000,000. [...] We train the model using contrastive divergence (Hinton, 2002), with k = 15 steps of blocked Gibbs updates, on 20, 000 such training images. |