Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
Authors: Geoffrey Roeder, Paul Grant, Andrew Phillips, Neil Dalchau, Edward Meeds
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate our method by predicting the dynamic behaviour of bacteria that were genetically engineered to function as biosensors. and 5. Experiments We performed two experiments on data from the synthetic biology case study, where measurements of six genetic devices (Pcat-Pcat, RS100-S32, RS100-S34, R33-S32, R33-S175 and R33-S34) were combined into a collection of 312 time-series. |
| Researcher Affiliation | Collaboration | 1Microsoft Research, Cambridge, United Kingdom 2Princeton University, Princeton, United States of America. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it contain an explicit code release statement or repository link. |
| Open Datasets | No | The paper describes using a custom dataset of '312 time-series' from bacterial cell cultures for its experiments, but it does not provide concrete access information (link, DOI, repository name, or formal citation) for this dataset to be publicly available. |
| Dataset Splits | Yes | To perform 4-fold cross-validation, black-box models take approximately 40 minutes and white-box models approximately 2 hrs. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'tensorflow' and 'Adam optimisation' but does not provide specific version numbers for these or any other ancillary software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | For q(z I|Y, g), we use the same encoder NN for both whitebox and black-box models: 10 1D convolutional filters, feeding 50 unit hidden layer with tanh activations; g is concatenated to the hiddens, which is then connected to the mean and variances outputs of q. Neural networks !+ all have 25 hidden units. During training we used a K=100 importance weighted auto-encoder (IWAE) estimator for gradient computation (Burda et al., 2015). We ran all experiments for 500 epochs using Adam optimisation (Kingma & Ba, 2014). We first ran 4-fold cross-validation with 500 epochs and batch size 36... |