Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Scalable Variational Inference in Log-supermodular Models
Authors: Josip Djolonga, Andreas Krause
ICML 2015 | Venue PDF | LLM Run Details | Input Tokens: 15,934 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,151 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments confirm scalability of our approach, high quality of the marginals, and the benefit of incorporating higher-order potentials. |
| Researcher Affiliation | Academia | Josip Djolonga EMAIL Andreas Krause EMAIL Department of Computer Science, ETH Zurich |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The code will be made available at http://people.inf.ethz.ch/josipd/. |
| Open Datasets | Yes | We used the data from Jegelka & Bilmes (2011), which contains a total of 36 images, each with a highly detailed (pixel-level precision) ground truth segmentation. |
| Dataset Splits | Yes | Then, we performed a leave-one-out crossvalidation for estimating the average AUC. |
| Hardware Specification | No | The reported typical running times are for an image of size 427x640 pixels on a quad core machine and we report the wall clock time of the inference code (without setting up the factor graph or generating the superpixels). This does not specify CPU/GPU model or other details. |
| Software Dependencies | No | We have used the implementation from lib DAI (Mooij, 2010). and solved using the total variation Douglas-Rachford (DR) code from (Barbero & Sra, 2011; 2014; Jegelka et al., 2013). Neither specifies version numbers for these software components. |
| Experiment Setup | Yes | The maximum number of iterations was set to 70. (for BP and MF) and We ran for at most 100 iterations. (for DR) and For every method we tested several variants using different combinations for α, β, γ and θ (exact numbers provided in the appendix). |