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].
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval
Authors: Fan Wu, Patrick Rebeschini
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the following, we present simulations showing how the parameter β affects the convergence of HWF. The setup for the simulations is as follows. We generate a 10-sparse signal vector x Rn with x min = Ω(1/√k) from e O(k2) Gaussian measurements, where e O hides logarithmic terms. To the best of our knowledge, this is the first result on continuous-time solvers for (sparse) phase retrieval. We sample m = 1000 Gaussian measurement vectors Aj N(0, I50000) and generate phaseless measurements Yj = (AT j x )2. |
| Researcher Affiliation | Academia | Fan Wu Department of Statistics University of Oxford EMAIL Patrick Rebeschini Department of Statistics University of Oxford EMAIL |
| Pseudocode | No | The paper provides mathematical equations describing the algorithms (e.g., equations 1, 11, 13, 14), but it does not present them as structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper describes generating synthetic data for its simulations ('We generate a 10-sparse signal vector... We sample m = 1000 Gaussian measurement vectors...'), rather than using a publicly available dataset with concrete access information such as a URL, DOI, or specific repository name. |
| Dataset Splits | No | The paper describes data generation for simulations but does not specify train, validation, or test dataset splits, nor does it mention cross-validation or predefined splits with citations. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the simulations, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers) that would be needed to replicate the experiments. |
| Experiment Setup | Yes | The setup for the simulations is as follows. We generate a 10-sparse signal vector x R50000 by first drawing x N(0, I50000), then setting 49990 random entries of x to zero, and finally normalizing the vector to x 2 = 1. We sample m = 1000 Gaussian measurement vectors Aj N(0, I50000) and generate phaseless measurements Yj = (AT j x )2. For the step size η, we follow [54] and choose η = 0.1. |