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.
Planar Ultrametrics for Image Segmentation
Authors: Julian E. Yarkony, Charless Fowlkes
NeurIPS 2015 | Venue PDF | LLM Run Details | Input Tokens: 13,759 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,776 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 | We apply our algorithm to the task of natural image segmentation and demonstrate that our algorithm converges rapidly and produces optimal or near-optimal solutions in practice. We applied our algorithm to segmenting images from the Berkeley Segmentation Data set (BSDS) [16]. |
| Researcher Affiliation | Collaboration | Julian Yarkony Experian Data Lab San Diego, CA 92130 EMAIL Charless C. Fowlkes Department of Computer Science University of California Irvine EMAIL |
| Pseudocode | Yes | Algorithm 1 Dual Closest Ultrametric via Cutting Planes |
| Open Source Code | No | The paper mentions using existing libraries (Blossom V, IBM ILOG CPLEX) but does not state that their own code for the described methodology is open-source or provide a link. |
| Open Datasets | Yes | We applied our algorithm to segmenting images from the Berkeley Segmentation Data set (BSDS) [16]. |
| Dataset Splits | No | The paper references the BSDS dataset but does not explicitly provide details about training, validation, or test dataset splits (percentages, counts, or specific predefined split usage). |
| 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 "MATLAB", "Blossom V implementation of minimum-weight perfect matching [15]" and "IBM ILOG CPLEX LP solver" but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We truncate extreme values to enforce that g Pbe [ϵ, 1 ϵ] with ϵ = 0.001 and set θe = log g P be 1 g P be. In our experiments we use a fixed set of eleven distance threshold levels {δl} chosen to uniformly span the useful range of threshold values [9.6, 12.6]. We performed dual cutting plane iterations until convergence or 2000 seconds had passed. ... We terminate when the total residual is greater than 2 10 4. In our experiments, this penalty is scaled by a parameter ϵ = 10 4. In our implementation we test a few discrete thresholds t {0, 0.2, 0.4, 0.6, 0.8} and take that threshold that yields X with the lowest cost. |