Gradient Descent Provably Optimizes Over-parameterized Neural Networks
Authors: Simon S. Du, Xiyu Zhai, Barnabas Poczos, Aarti Singh
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 EXPERIMENTS In this section, we use synthetic data to corroborate our theoretical findings. |
| Researcher Affiliation | Academia | Simon S. Du Machine Learning Department Carnegie Mellon University ssdu@cs.cmu.edu Xiyu Zhai Department of EECS Massachusetts Institute of Technology xiyuzhai@mit.edu Barnab as Pocz os Machine Learning Department Carnegie Mellon University bapozos@cs.cmu.edu Aarti Singh Machine Learning Department Carnegie Mellon University aartisingh@cmu.edu |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm', nor are there structured steps formatted like code. |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | No | We use synthetic data to corroborate our theoretical findings. We uniformly generate n = 1000 data points from a d = 1000 dimensional unit sphere and generate labels from a one-dimensional standard Gaussian distribution. |
| Dataset Splits | No | The paper states 'We use synthetic data to corroborate our theoretical findings.' but does not specify any dataset splits (training, validation, test) or cross-validation setup. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instances) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | For all experiments, we run 100 epochs of gradient descent and use a fixed step size. |