Lost Relatives of the Gumbel Trick
Authors: Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted experiments with the following aims: |
| Researcher Affiliation | Collaboration | 1University of Cambridge, UK 2MPI-IS, T ubingen, Germany 3UC Berkeley, USA 4Uber AI Labs, USA 5Alan Turing Institute, UK. |
| Pseudocode | Yes | Algorithm 1 Sequential sampler for Gibbs distribution |
| Open Source Code | Yes | Code: https://github.com/matejbalog/gumbel-relatives. |
| Open Datasets | Yes | Figure 4 shows the MSEs of U( ) as estimators of ln Z on 10 10 (n = 100) binary pairwise grid models with unary potentials sampled uniformly from [ 1, 1] and pairwise potentials from [0, C] (attractive models) or from [ C, C] (mixed models), for varying coupling strengths C. |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions "using lib DAI (Mooij, 2010)" but does not specify a version number for this or any other software dependency. |
| Experiment Setup | Yes | Figure 4 shows the MSEs of U( ) as estimators of ln Z on 10 10 (n = 100) binary pairwise grid models with unary potentials sampled uniformly from [ 1, 1] and pairwise potentials from [0, C] (attractive models) or from [ C, C] (mixed models), for varying coupling strengths C. We replaced the expectations in U( ) s with sample averages of size M = 100, using lib DAI (Mooij, 2010) to solve the MAP problems yielding these samples. |