Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Non-parametric Structured Output Networks
Authors: Andreas Lehrmann, Leonid Sigal
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate non-parametric structured output networks at both the model (DNN+NGM) and the inference level (RIN). Model validation consists of a comparison to baselines along two binary axes, structuredness and non-parametricity. Inference validation compares our RIN unit to the two predominant groups of approaches for inference in structured non-parametric densities, i.e., sampling-based and variational inference (Section 1.1.2). [...] Table 2: Quantitative Evaluation. |
| Researcher Affiliation | Industry | Andreas M. Lehrmann Disney Research Pittsburgh, PA 15213 EMAIL Leonid Sigal Disney Research Pittsburgh, PA 15213 EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We test our approach on simple natural pixel statistics from Microsoft COCO [11] by sampling stripes y = (yi)n i=1 2 [0, 255]n of n = 10 pixels. [11] Lin, T.Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C.L., Dollar, P.: Microsoft COCO: Common Objects in Context. In ar Xiv:1405.0312 [cs.CV]. (2014) |
| Dataset Splits | No | Using this noise process, we generate training and test sets of sizes 100,000 and 1,000, respectively. The paper does not explicitly specify a validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We train the other 4 models for 40 epochs using a Gaussian kernel and a diagonal bandwidth matrix for the non-parametric models. The DNN consists of 2 fully-connected layers with 256 units and the kernel weights are constrained to lie on a simplex with a softmax layer. The NGM uses a chain-structured graph that connects each pixel to its immediate neighbors. |