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..
Sparse Bayesian Generative Modeling for Compressive Sensing
Authors: Benedikt Böck, Sadaf Syed, Wolfgang Utschick
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support our approach theoretically through the concept of variational inference and validate it empirically using different types of compressible signals. and 4 Experiments |
| Researcher Affiliation | Academia | Benedikt Böck, Sadaf Syed, Wolfgang Utschick TUM School of Computation, Information and Technology Technical University of Munich EMAIL |
| Pseudocode | Yes | N Pseudo-Code for the Training and Inference of the CSVAE and CSGMM |
| Open Source Code | Yes | 1Source code is available at https://github.com/beneboeck/sparse-bayesian-gen-mod. |
| Open Datasets | Yes | We use the MNIST dataset (N = 784) for evaluation [42, (CC-BY-SA 3.0 license)]. and We also use a dataset of 64 × 64 cropped celeb A images (N = 3 × 642 = 12288) [44] and evaluate on the Fashion MNIST dataset (N = 784) in Appendix L [45, (MIT license)]. |
| Dataset Splits | Yes | We once reduce the learning rate by a factor of 2 during training and stop the training, when the modified ELBO in (15) for a validation set of 5000 samples does not increase. |
| Hardware Specification | Yes | All models have been simulated on an NVIDIA A40 GPU except for the proposed CSGMM, whose experiments have been conducted on an Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz. |
| Software Dependencies | No | The paper mentions software like 'Adam' for optimization and 'Pywavelets' for wavelet analysis, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For CSGMM, we set the number K of components to 32... The CSVAE encoders and decoders contain two fully-connected layers with Re LU activation... The latent dimension is set to 16, the learning rate is set to 2 × 10−5, and the batch size is set to 64. We use the Adam optimizer for optimization [47]. |