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].
Global Guarantees for Blind Demodulation with Generative Priors
Authors: Paul Hand, Babhru Joshi
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now empirically show that Algorithm 1 can remove distortions present in the dataset. We consider the image recovery task of removing distortions that were synthetically introduced to the MNIST dataset. |
| Researcher Affiliation | Academia | Paul Hand Dept. of Mathematics and College of Computer Science and Information Northeastern University, MA EMAIL Babhru Joshi Dept. of Mathematics University of British Columbia, BC EMAIL |
| Pseudocode | Yes | Algorithm 1 Alternating descent algorithm for (2) |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We consider the image recovery task of removing distortions that were synthetically introduced to the MNIST dataset. Prior to training the generators, the images in the MNIST dataset and the distortion dataset were resized to 64x64 images. |
| Dataset Splits | No | The paper mentions training generators on the MNIST dataset but does not provide explicit details about training, validation, or test dataset splits (e.g., percentages, sample counts, or predefined splits). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using DCGAN but does not provide specific version numbers for this or any other software dependencies, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | We used DCGAN with a learning rate of 0.0002 and latent code dimension of 50... We used the Stochastic Gradient Descent algorithm with the step size set to 1 and momentum set to 0.9. ... after 500 iterations. |