Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Compressed Sensing using Generative Models
Authors: Ashish Bora, Ajil Jalal, Eric Price, Alexandros G. Dimakis
ICML 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,688 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,751 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare the performance of our algorithm with baselines. We show a plot of per pixel reconstruction error as we vary the number of measurements. The vertical bars indicate 95% confidence intervals. |
| Researcher Affiliation | Academia | 1University of Texas at Austin, Department of Computer Science 2University of Texas at Austin, Department of Electrical and Computer Engineering. |
| Pseudocode | No | The paper describes the algorithm in Section 2 'Our Algorithm' using prose, but it does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | Code for experiments in the paper can be found at: https://github.com/Ashish Bora/csgm |
| Open Datasets | Yes | The MNIST dataset consists of about 60, 000 images of handwritten digits, where each image is of size 28 28 (Le Cun et al., 1998). ... Celeb A is a dataset of more than 200, 000 face images of celebrities (Liu et al., 2015). |
| Dataset Splits | No | The paper mentions using a 'held out test set' but does not specify explicit training/validation/test splits (e.g., percentages or sample counts) or cross-validation setup. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and implies a TensorFlow implementation through a reference (Kim, 2017), but it does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We train the VAE using the Adam optimizer (Kingma & Ba, 2014) with a mini-batch size 100 and a learning rate of 0.001. We use λ = 0.1 in Eqn. (3). ... Each update used the Adam optimizer (Kingma & Ba, 2014) with minibatch size 64, learning rate 0.0002 and β1 = 0.5. We use λ = 0.001 in Eqn. (3). |