Efficient identification of informative features in simulation-based inference
Authors: Jonas Beck, Michael Deistler, Yves Bernaerts, Jakob H Macke, Philipp Berens
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For a simple linear Gaussian model and non-linear HH models, we show that the obtained posterior estimates are as accurate and robust as when repeatedly retraining density estimators from scratch, despite being much faster to compute. We then apply our algorithm to study which features are the most useful for constraining posteriors over parameters of the HH model. |
| Researcher Affiliation | Academia | Jonas Beck University of Tübingen jonas.beck@uni-tuebingen.de Michael Deistler University of Tübingen michael.deistler@uni-tuebingen.de Yves Bernaerts University of Tübingen yves.bernaerts@uni-tuebingen.de Jakob H. Macke University of Tübingen Max Planck Institute for Intelligent Systems jakob.macke@uni-tuebingen.de Philipp Berens University of Tübingen philipp.berens@uni-tuebingen.de |
| Pseudocode | No | The paper does not contain a pseudocode block or an explicitly labeled algorithm. |
| Open Source Code | Yes | We implement FSLM using python 3.8, building on top of the public sbi library [36]. All the code is available at github.com/berenslab/fslm_repo. |
| Open Datasets | No | The paper uses simulated data generated from a mechanistic model rather than a publicly available dataset. "Parameters are sampled from the prior and used to simulate a synthetic dataset." and "To train the density estimator we simulated 1 million observations with parameters drawn from a Uniform prior over biologically plausible parameter ranges (see A.3)." |
| Dataset Splits | Yes | Training was terminated when the log-likelihoods had stopped improving on a validation set that was split from the training set at 10% over 20 epochs. |
| Hardware Specification | Yes | All computations were done on an internal cluster running Intel(R) Xeon(R) Gold 6226R CPUs @ 2.90GHz. |
| Software Dependencies | No | The paper mentions 'python 3.8' and 'sbi library [36]' but only provides a version number for Python, not for other key software components or libraries. |
| Experiment Setup | Yes | The MDNs had three hidden layers and 10 mixture components. The MAF consisted of five 3 layer deep MADEs [43]. Training was done on 10,000 samples, then 500 samples were drawn from each posterior, for a total of 10 different initializations. Training was terminated when the log-likelihoods had stopped improving on a validation set that was split from the training set at 10% over 20 epochs. |