Non-Gaussian Tensor Programs
Authors: Eugene Golikov, Greg Yang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate Corollaries 4.3 and 4.4 in Appendix N. If you ran experiments...Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? [Yes] See plots in Appendix N. |
| Researcher Affiliation | Collaboration | Eugene Golikov Ecole Polytechnique F ed erale de Lausanne Lausanne, Switzerland evgenii.golikov@epfl.ch Greg Yang Microsoft Research Seattle, USA gregyang@microsoft.com |
| Pseudocode | No | The paper describes mathematical frameworks and theorems but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper's reproducibility checklist states "Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes]". However, the main body or appendices of the paper do not provide a direct link or explicit statement of where this code is available (e.g., a specific repository URL or a mention of its inclusion in supplementary material files). |
| Open Datasets | Yes | Our results are obtained by training a deep MLP with width 1024 on the CIFAR-10 dataset |
| Dataset Splits | No | The paper mentions training on the CIFAR-10 dataset but does not specify the training, validation, and test splits (e.g., percentages or exact counts). While CIFAR-10 has standard splits, these are not explicitly stated within the paper. |
| Hardware Specification | Yes | We performed our experiments on a single NVIDIA A6000 GPU. |
| Software Dependencies | No | Our implementation is based on PyTorch. However, no specific version number for PyTorch or any other software dependencies is provided. |
| Experiment Setup | Yes | We trained a deep MLP with width 1024 on the CIFAR-10 dataset... using an Adam optimizer... with a learning rate of 1e-3, batch size of 256, and trained for 100 epochs. |