The Geometry of Deep Networks: Power Diagram Subdivision
Authors: Randall Balestriero, Romain Cosentino, Behnaam Aazhang, Richard Baraniuk
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 figures). |
| 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. |