Mean Field Residual Networks: On the Edge of Chaos
Authors: Ge Yang, Samuel Schoenholz
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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 gregyang@microsoft.com Samuel S. Schoenholz Google Brain schsam@google.com 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). |