Cooperative Graphical Models
Authors: Josip Djolonga, Stefanie Jegelka, Sebastian Tschiatschek, Andreas Krause
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 josipd@inf.ethz.ch Stefanie Jegelka CSAIL, MIT stefje@mit.edu Sebastian Tschiatschek Dept. of Computer Science, ETH Z urich stschia@inf.ethz.ch Andreas Krause Dept. of Computer Science, ETH Z urich krausea@inf.ethz.ch |
| 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).' |