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.
Min-Max Propagation
Authors: Christopher Srinivasa, Inmar Givoni, Siamak Ravanbakhsh, Brendan J. Frey
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,241 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,459 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 | 5 Experiments and Applications In the first part of this section we compare min-max propagation with the only alternative min-max inference method over FGs that relies on sum-product reduction. In the second part, we formulate the real-world problem of makespan minimization as a min-max inference problem, with high-order factors. ... For N = 10 we also report the exact min-max solutions. ... Results. Fig. 4 compares the performance of sum-product reduction that relies on PBP with min-max propagation and brute-force. For min-max propagation we report the results for three different decimation procedures. |
| Researcher Affiliation | Collaboration | Christopher Srinivasa University of Toronto Borealis AI Inmar Givoni University of Toronto Siamak Ravanbakhsh University of British Columbia Brendan J. Frey University of Toronto Vector Institute Deep Genomics |
| Pseudocode | No | No structured pseudocode or algorithm blocks are present. Section 4 describes a |
| Open Source Code | No | The paper does not contain any statement about making the source code available, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper refers to |
| Dataset Splits | No | The paper does not specify exact training, validation, or test dataset splits (percentages or counts), nor does it describe a cross-validation setup or reference predefined splits with specific citations or access details for partitioning the data. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, processors, memory, or cloud computing instance types) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions 'Perturbed Belief Propagation (PBP)' and 'IBM s CPLEX solver' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Min-max propagation is run for a maximum T = 1000 iterations or until convergence, whichever comes first. ... The PBP used in the sum-product reduction requires a fixed T; we report the results for T equal to the worse case min-max convergence iterations (see appendix) and T = 1000 iterations. |