How Many Samples are Needed to Estimate a Convolutional Neural Network?
Authors: Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Russ R. Salakhutdinov, Aarti Singh
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5, we use numerical experiments to verify our theoretical findings. |
| Researcher Affiliation | Academia | Simon S. Du Carnegie Mellon University Yining Wang* Carnegie Mellon University Xiyu Zhai Massachusetts Institute of Technology Sivaraman Balakrishnan Carnegie Mellon University Ruslan Salakhutdinov Carnegie Mellon University Aarti Singh Carnegie Mellon University |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code or a link to a code repository. |
| Open Datasets | No | For all experiments, we let the ambient dimension d be 64 and the input distribution be Gaussian with mean 0 and identity covariance. This describes how data was generated rather than providing access to a publicly available dataset. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test splits. It mentions "Number of Training Data" on the x-axis of plots, but no split percentages or counts are given. |
| Hardware Specification | No | The paper does not mention any specific hardware specifications (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions that for all experiments, the ambient dimension d is 64 and the input distribution is Gaussian. It also specifies filter sizes and stride sizes for different experiments (e.g., "Filter size m 2", "stride size s 1"). However, it does not explicitly provide hyperparameters like learning rates, batch sizes, optimizers, or number of epochs, which are typical for an experimental setup. |