Core Dependency Networks
Authors: Alejandro Molina, Alexander Munteanu, Kristian Kersting
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 alejandro.molina@tu-dortmund.de CS Department TU Dortmund, Germany; Alexander Munteanu alexander.munteanu@tu-dortmund.de CS Department TU Dortmund, Germany; Kristian Kersting kersting@cs.tu-darmstadt.de 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. |