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.
Cooperative Graphical Models
Authors: Josip Djolonga, Stefanie Jegelka, Sebastian Tschiatschek, Andreas Krause
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 16,044 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,770 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 | Finally, we demonstrate the efficacy of our methods empirically. Our experiments indicate the scalability, efficacy and quality of these schemes. (Section 6: Experiments) |
| Researcher Affiliation | Academia | Josip Djolonga Dept. of Computer Science, ETH Z urich EMAIL Stefanie Jegelka CSAIL, MIT EMAIL Sebastian Tschiatschek Dept. of Computer Science, ETH Z urich EMAIL Andreas Krause Dept. of Computer Science, ETH Z urich EMAIL |
| Pseudocode | Yes | Figure 2: Inference with Frank-Wolfe, assuming that VAR-INFERENCE guarantees an upper bound. 1: procedure FW-INFERENCE(f, θ) ... 1: procedure LINEAR-ORACLE(f, τ) |
| Open Source Code | Yes | We use the same setting, data and models as [1], as implemented in the pycoop5 package. (footnote 5: https://github.com/shelhamer/coop-cut.) |
| Open Datasets | No | For the image segmentation task, the paper states: 'We use the same setting, data and models as [1], as implemented in the pycoop5 package.' However, it does not provide a direct link, DOI, or specific instructions for accessing this dataset within the paper. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., percentages or sample counts) for any of its experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions software components such as 'lib DAI [34]', 'cvxpy [35]', 'SCS [36]', and a 'max-flow solver [37]'. However, it does not provide specific version numbers for these software dependencies, which are required for reproducible software description. |
| Experiment Setup | Yes | For synthetic experiments: 'The unary potentials were sampled as θi(xi) Uniform( α, α). The edges E were randomly split into five disjoint buckets E1, E2, . . . , E5, and we used f(y) = P5 j=1 hj(y Ej), where y Ei are the coordinates of y corresponding to that group, and the functions {hj} will be defined below.' and 'hi(y Ei) = wi q P |Ei|, with weights wi Uniform(0, β).' and 'θi,j(xi, xj) = wi,j Jxi = xj K, where wi,j Uniform( β, β).' |