Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Deep Information Propagation
Authors: Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein
ICLR 2017 | Venue PDF | LLM Run Details | Input Tokens: 18,711 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 1,988 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To test this ansatz we train ensembles of deep, fully connected, feed-forward neural networks of varying depth on MNIST and CIFAR10, with and without dropout. Our results con๏ฌrm that neural networks are trainable precisely when their depth is not much larger than ฮพc. |
| Researcher Affiliation | Collaboration | Samuel S. Schoenholz Google Brain Justin Gilmer Google Brain Surya Ganguli Stanford University Jascha Sohl-Dickstein Google Brain |
| 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 | To test this ansatz we train ensembles of deep, fully connected, feed-forward neural networks of varying depth on MNIST and CIFAR10, with and without dropout. |
| Dataset Splits | No | The paper mentions training on MNIST and CIFAR10, but does not explicitly provide specific dataset split information (percentages or counts) or reference predefined splits for validation. |
| 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 | Yes | We train these networks using Stochastic Gradient Descent (SGD) and RMSProp on MNIST and CIFAR10. We use a learning rate of 10-3 for SGD when L <= 200, 10-4 for larger L, and 10-5 for RMSProp. These learning rates were selected by grid search between 10-6 and 10-2 in exponentially spaced steps of size 10. |