Annealed Flow Transport Monte Carlo
Authors: Michael Arbel, Alex Matthews, Arnaud Doucet
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate experimentally the benefits and limitations of our methodology on a variety of applications. |
| Researcher Affiliation | Collaboration | 1Gatsby Computational Neuroscience Unit, University College London 2Deep Mind. Correspondence to: Michael Arbel <michael.n.arbel@gmail.com>, Alexander G. D. G. Matthews <alexmatthews@google.com>, Arnaud Doucet <arnauddoucet@google.com>. |
| Pseudocode | Yes | Algorithm 1 Annealed Flow Transport |
| Open Source Code | No | We plan to make the code available within https://github.com/deepmind. |
| Open Datasets | Yes | For our next example, we trained a variational autoencoder (Kingma and Welling, 2014; Rezende et al., 2014) with convolution on the binarized MNIST dataset (Salakhutdinov and Murray, 2008) |
| Dataset Splits | No | As discussed in Section 3.3, we use three sets of particles—train, test and validation which improves robustness, avoids overfitting the flow to the particles and gives unbiased estimates of Z when using the test set. The paper states that validation sets are used but does not provide specific split percentages or counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or cloud instance specifications used for running the experiments. It only mentions "modern hardware" in general terms. |
| Software Dependencies | No | The paper mentions various software components and libraries (e.g., JAX, Haiku, Optax, TensorFlow Distributions, Adam) but does not provide specific version numbers for these dependencies, which are necessary for reproducibility. |
| Experiment Setup | Yes | We tune the step size to have a reasonable acceptance probability based on preliminary runs of SMC using a modest K. Then for larger K experiments, we linearly interpolate the step sizes chosen on the preliminary runs. We always use a linearly spaced geometric schedule and the initial distribution is always a multivariate standard normal. We repeat experiments 100 times. For the AFT flow we used an affine transformation with diagonal linear transformation matrix. |