Amortized Inference for Heterogeneous Reconstruction in Cryo-EM
Authors: Axel Levy, Gordon Wetzstein, Julien N.P Martel, Frederic Poitevin, Ellen Zhong
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate cryo FIRE for ab initio heterogeneous reconstruction and compare it with the state-of-the-art method cryo DRGN2 [39]. We first validate that using an encoder to predict poses, instead of performing an exhaustive pose search, enables us to reduce the runtime of heterogeneous reconstruction on a synthetic dataset. We show that the encoder is able to accurately predict ϕi and zi for images it has never processed during training, thereby validating the ability of an encoder-like architecture to amortize the runtime over the size of the dataset. |
| Researcher Affiliation | Academia | Axel Levy Stanford University Gordon Wetzstein Stanford University Julien Martel Stanford University Frédéric Poitevin SLAC National Accelerator Laboratory Ellen D. Zhong Princeton University Correspondence to: zhonge@princeton.edu |
| Pseudocode | No | The paper describes the architecture and training procedures in text, but it does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | By providing an open-source implementation of Cryo FIRE upon publication, together with benchmark metrics, we hope to make cryo-EM research accessible to a broader class of researchers." and "Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] We plan on making code available. |
| Open Datasets | Yes | We use the publicly available dataset EMPIAR-10180 [21] of a pre-catalytic spliceosome (Supplement C). |
| Dataset Splits | No | Table 1 shows dataset sizes for training and testing (e.g., 'Small (Train: 50k / Test: 10k)'), but a separate validation dataset split with its size is not explicitly provided. |
| Hardware Specification | Yes | We train the models on a single NVIDIA A100 SXM4 40GB GPU. |
| Software Dependencies | No | The paper mentions using the ADAM optimizer, but it does not specify software dependencies like programming languages, libraries, or frameworks with specific version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | With cryo FIRE, we fix d = 8 and activate the conformation MLP after the model has seen 1.5M images... Images of size D = 128 are fed by batches of maximum sizes (128 for cryo FIRE, 32 for cryo DRGN2)... The model is optimized with the ADAM optimizer [10] and a learning rate of 10 4. |