Masked Autoregressive Flow for Density Estimation
Authors: George Papamakarios, Theo Pavlakou, Iain Murray
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate MAF on a wide range of datasets, and we demonstrate that (a) MAF outperforms Real NVP on general-purpose density estimation, and (b) a conditional version of MAF achieves close to state-of-the-art performance on conditional image modelling even with a general-purpose architecture. |
| Researcher Affiliation | Academia | George Papamakarios University of Edinburgh g.papamakarios@ed.ac.uk Theo Pavlakou University of Edinburgh theo.pavlakou@ed.ac.uk Iain Murray University of Edinburgh i.murray@ed.ac.uk |
| Pseudocode | No | The paper describes algorithms and transformations using mathematical equations and descriptive text, but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code for reproducing our experiments (which uses Theano [29]) can be found at https://github.com/gpapamak/maf. |
| Open Datasets | Yes | Following Uria et al. [32], we perform unconditional density estimation on four UCI datasets (POWER, GAS, HEPMASS, MINIBOONE) and on a dataset of natural image patches (BSDS300). ... For conditional density estimation, we used the MNIST dataset of handwritten digits [17] and the CIFAR-10 dataset of natural images [14]. |
| Dataset Splits | Yes | Each model was trained with early stopping until no improvement occurred for 30 consecutive epochs on the validation set. For each model, we selected the number of hidden layers and number of hidden units based on validation performance |
| Hardware Specification | No | The paper mentions "parallel computing architectures such as Graphics Processing Units (GPUs)" but does not specify any particular GPU models or other hardware details used for the experiments. |
| Software Dependencies | No | The paper mentions using "Theano [29]" but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | All models were trained with the Adam optimizer [11], using a minibatch size of 100, and a step size of 10 3 for MADE and MADE Mo G, and of 10 4 for Real NVP and MAF. A small amount of ℓ2 regularization was added, with coefficient 10 6. |