Sparse Fourier Backpropagation in Cryo-EM Reconstruction
Authors: Dari Kimanius, Kiarash Jamali, Sjors Scheres
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We achieve improved results on a simulated data set and at least equivalent results on an experimental data set when compared to the coordinate-based approach, while also substantially lowering computational cost. |
| Researcher Affiliation | Academia | Dari Kimanius Kiarash Jamali Sjors H.W. Scheres MRC Laboratory of Molecular Biology {dari, kjamali, scheres}@mrc-lmb.cam.ac.uk |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The code used in this paper is available at github.com/dkimanius/sbackprop, together with the instructions for compiling and running it. |
| Open Datasets | Yes | This data set, along with the PDB files used to create it and the refinement results, can be found at zenodo.org/record/7182156 (DOI:10.5281/zenodo.7182156). |
| Dataset Splits | No | The paper does not explicitly provide training, validation, or test dataset splits. It mentions the total number of images in the datasets but not how they were partitioned for training or evaluation. |
| Hardware Specification | Yes | All calculations were performed on an NVIDIA RTX 3090. |
| Software Dependencies | No | The paper mentions "Py Torch [21] with custom CUDA kernels" but does not specify version numbers for PyTorch or CUDA, which are necessary for reproducible software dependencies. |
| Experiment Setup | Yes | We trained our VAE with 8 latent dimensions for 50 epochs... We used 10 latent dimensions for this data set. |