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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Neural Proximal Gradient Descent for Compressive Imaging
Authors: Morteza Mardani, Qingyun Sun, David Donoho, Vardan Papyan, Hatef Monajemi, Shreyas Vasanawala, John Pauly
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are carried out under different settings: (a) reconstructing abdominal MRI of pediatric patients from highly undersampled Fourier-space data and (b) superresolving natural face images. |
| Researcher Affiliation | Academia | Depts. of 1Electrical Eng., 2Radiology, 3Statistics, and 4Mathematics; Stanford University |
| Pseudocode | No | The paper describes the iterative procedure in mathematical equations and prose but does not provide a formal pseudocode block or algorithm box. |
| Open Source Code | Yes | The source code for Tensor Flow implementation is publicly available in the Github page [35]. |
| Open Datasets | Yes | Adopting celeb Faces Attributes Dataset (Celeb A) [40], for training and test we use 10K and 1, 280 images, respectively. |
| Dataset Splits | No | The paper mentions 'train dataset' and 'test dataset' with specific counts, but does not explicitly describe a validation split. |
| Hardware Specification | Yes | Training was performed with Tensor Flow interface on an NVIDIA Titan X Pascal GPU with 12GB RAM. |
| Software Dependencies | No | The paper mentions 'Tensor Flow interface' but does not specify its version number or any other software dependencies with versions. |
| Experiment Setup | Yes | We used the Adam SGD optimizer with the momentum parameter 0.9, mini-batch size 2, and initial learning rate 10 5 that is halved every 10K iterations. For training RNN, we use ℓ2 cost in (P2) with β = 0.75. |