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 [1].
The Loss Surface of Deep and Wide Neural Networks
Authors: Quynh Nguyen, Matthias Hein
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that this is (almost) true, in fact almost all local minima are globally optimal, for a fully connected network with squared loss and analytic activation function given that the number of hidden units of one layer of the network is larger than the number of training points and the network structure from this layer on is pyramidal. |
| Researcher Affiliation | Academia | Quynh Nguyen 1 Matthias Hein 1 1Department of Mathematics and Computer Science, Saarland University, Germany. Correspondence to: Quynh Nguyen <EMAIL>. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe experiments on a specific, publicly available dataset with concrete access information. |
| Dataset Splits | No | This is a theoretical paper and does not describe experiments that would involve dataset splits. |
| Hardware Specification | No | The paper does not describe any experiments, and therefore no specific hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers for reproducing experiments. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, such as hyperparameters or system-level training settings. |