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].
SGD Learns the Conjugate Kernel Class of the Network
Authors: Amit Daniely
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | From an empirical perspective, in [Daniely et al., 2017], it is shown that for standard convolutional networks the conjugate class contains functions whose performance is close to the performance of the function that is actually learned by the network. This is based on experiments on the standard CIFAR-10 dataset. |
| Researcher Affiliation | Collaboration | Amit Daniely Hebrew University and Google Research EMAIL |
| Pseudocode | Yes | Algorithm 1 Generic Neural Network Training |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | This is based on experiments on the standard CIFAR-10 dataset. |
| Dataset Splits | No | The paper mentions using CIFAR-10 for empirical perspective but does not specify details of training, validation, or test splits. The focus is on theoretical guarantees, not empirical reproduction details. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | While Algorithm 1 and the theorems define parameters and conditions (e.g., learning rate η, batch size m), they do not provide specific numerical values for hyperparameters or concrete system-level training settings as would typically be found in an experimental setup description. |