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..
Don't take it lightly: Phasing optical random projections with unknown operators
Authors: Sidharth Gupta, Remi Gribonval, Laurent Daudet, Ivan Dokmanić
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test the proposed MPR algorithm via simulations and experiments on a real OPU. |
| Researcher Affiliation | Collaboration | Sidharth Gupta University of Illinois at Urbana-Champaign EMAIL Rémi Gribonval Univ Rennes, Inria, CNRS, IRISA EMAIL Laurent Daudet Light On, Paris EMAIL Ivan Dokmani c University of Illinois at Urbana-Champaign EMAIL |
| Pseudocode | Yes | Algorithm 1 MPR algorithm for S frames. |
| Open Source Code | Yes | Reproducible code available at https://github.com/swing-research/opu_phase under the MIT License. |
| Open Datasets | Yes | Next, we take 500 28 x 28 samples from the MNIST dataset [13], threshold them to be binary, vectorize them, and stack them into a matrix B ∈ R500x282. |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits for the MNIST dataset used. |
| Hardware Specification | No | The paper mentions using a 'publicly available cloud-based OPU' and describes components like DMD and cameras, but it does not specify concrete hardware details such as CPU/GPU models, memory, or processor types used for computation. |
| Software Dependencies | No | The paper mentions 'scikit-learn interface' and 'Python' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In all simulations, intensity measurements are quantized to 8 bits and all signals and references are iid standard (complex) Gaussian random vectors. We use 5 anchors in all RSVD experiments. The empirically determined sensitivity threshold of the camera is τ = 6. |