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..
Modeling Sparse Deviations for Compressed Sensing using Generative Models
Authors: Manik Dhar, Aditya Grover, Stefano Ermon
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we observe consistent improvements in reconstruction accuracy over competing approaches, especially in the more practical setting of transfer compressed sensing where a generative model for a data-rich, source domain aids sensing on a data-scarce, target domain. and 5. Experimental Evaluation |
| Researcher Affiliation | Academia | 1Computer Science Department, Stanford University, CA, USA. |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We considered the MNIST dataset of handwritten digits (Le Cun et al., 2010) and the OMNIGLOT dataset of handwritten characters (Lake et al., 2015). |
| Dataset Splits | Yes | For VAE training, we used the standard train/held-out splits of both datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions general software or libraries (e.g., 'Tensorflow' is cited in references) but does not provide specific version numbers for software dependencies needed to replicate the experiment. |
| Experiment Setup | Yes | The architecture and other hyperparameter details are given in the Appendix. |