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..
Mean Field Residual Networks: On the Edge of Chaos
Authors: Ge Yang, Samuel Schoenholz
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, for each activation function we study here, we initialize residual networks with different hyperparameters and train them on MNIST. |
| Researcher Affiliation | Collaboration | Microsoft Research AI EMAIL Samuel S. Schoenholz Google Brain EMAIL Work done while at Harvard University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | In our experiments, for each activation function we study here, we initialize residual networks with different hyperparameters and train them on MNIST. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or predefined splits) for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) 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 | Yes | We train a grid of reduced and full tanh resnets on MNIST, varying the variance σ2 w and the number of layers (for FRN we fix σv = 1). |