Signal Recovery with Non-Expansive Generative Network Priors
Authors: Jorio Cocola
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Appendix H we empirically verify the predictions of Theorem 5.4, demonstrating how (a practical variant of) Algorithm 1 recover signals y? in the range of non-expansive generative networks from undersampled noisy measurements. |
| Researcher Affiliation | Academia | Jorio Cocola Harvard University jcocola@seas.harvard.edu |
| Pseudocode | Yes | Algorithm 1: SUBGRADIENT DESCENT [21] |
| Open Source Code | No | The paper states 'Yes' to the question 'Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)?' in the checklist section 3a, but does not provide a direct link or explicit statement of code availability within the main body of the paper. |
| Open Datasets | No | The paper does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | All the experiments were run locally on a Apple M1 CPU |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes theoretical conditions for the algorithm's performance but does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. |