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..
Core Dependency Networks
Authors: Alejandro Molina, Alexander Munteanu, Kristian Kersting
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To corroborate our theoretical results, we empirically evaluated the resulting Core DNs on real data sets. The results demonstrate significant gains over no or naive sub-sampling, even in the case of count data. |
| Researcher Affiliation | Academia | Alejandro Molina EMAIL CS Department TU Dortmund, Germany; Alexander Munteanu EMAIL CS Department TU Dortmund, Germany; Kristian Kersting EMAIL CS Department and Centre for Cognitive Science TU Darmstadt, Germany |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | To this aim, we implemented (C)DNs in Python1. All experiments ran on a Linux machine (56 cores, 4 GPUs, and 512GB RAM). All DNs were trained using Iteratively reweighted least squares (IRWLS), however, coresets do not depend on the learning algorithm used. 1https://github.com/alejandromolinaml/Core DNs |
| Open Datasets | Yes | We used the MNIST2 data set of handwritten labeled digits. ... 2http://yann.lecun.com/exdb/mnist/ The second dataset contains traffic count measurements on selected roads around the city of Cologne in Germany (Ide et al. 2015). ... NIPS3 bag-of-words dataset. ... 3https://archive.ics.uci.edu/ml/datasets/bag+of+words |
| Dataset Splits | Yes | For each dataset, we performed ten fold cross-validation for training a full DN (Full) using all the data, leverage score sampling coresets (CDNs), and uniform samples (Uniform), for different sample sizes. |
| Hardware Specification | Yes | All experiments ran on a Linux machine (56 cores, 4 GPUs, and 512GB RAM). |
| Software Dependencies | No | The paper states 'implemented (C)DNs in Python' but does not provide specific version numbers for Python or any other software libraries or dependencies. |
| Experiment Setup | No | The paper mentions that 'All DNs were trained using Iteratively reweighted least squares (IRWLS)' but does not provide specific hyperparameters such as learning rate, batch size, or number of epochs. |