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..
The Geometry of Deep Networks: Power Diagram Subdivision
Authors: Randall Balestriero, Romain Cosentino, Behnaam Aazhang, Richard Baraniuk
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerous numerical experiments support and extend our theoretical results. |
| Researcher Affiliation | Academia | Randall Balestriero, Romain Cosentino, Behnaam Aazhang, Richard G. Baraniuk Rice University Houston, Texas, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any unambiguous statement about releasing code for the methodology described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | Figure 3 visualizes the centroids of the cell containing a particular input signal for a Large Conv and Res Net DN trained on the CIFAR10 dataset (see Appendix C for details on the models plus additional ο¬gures). |
| Dataset Splits | No | Fig. 4 (and 6 in the Appendix) plots the distributions of the log distances from the training points in the CIFAR10 training set to their nearest region boundary... Red: Test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (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 | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. |