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.
Experimental design for MRI by greedy policy search
Authors: Tim Bakker, Herke van Hoof, Max Welling
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 18,127 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,475 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 | Unexpectedly, our experiments show that a simple greedy approximation of the objective leads to solutions nearly on-par with the more general non-greedy approach. We offer a partial explanation for this phenomenon rooted in greater variance in the non-greedy objective’s gradient estimates, and experimentally verify that this variance hampers non-greedy models in adapting their policies to individual MR images. We empirically show that this adaptivity is key to improving subsampling designs. |
| Researcher Affiliation | Academia | Tim Bakker, University of Amsterdam, EMAIL Herke van Hoof, University of Amsterdam EMAIL Max Welling, University of Amsterdam, CIFAR EMAIL |
| Pseudocode | No | The paper describes procedures but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/Timsey/pg_mri. |
| Open Datasets | Yes | Datasets: We leverage the NYU fast MRI open database containing a large number of knee and brain volumes for our experiments [47] |
| Dataset Splits | Yes | This leads to a dataset of 6959 train slices, 1779 validation slices, and 1715 test slices. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like the U-Net baseline from the fast MRI repository and Weights&Biases tracking software, but does not provide specific version numbers for any key software components or libraries. |
| Experiment Setup | Yes | Models are trained for 50 epochs using a batch size of 16. |