Neural Spline Flows
Authors: Conor Durkan, Artur Bekasov, Iain Murray, George Papamakarios
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that neural spline flows improve density estimation, variational inference, and generative modeling of images.In our experiments, the neural network NN which computes the parameters of the elementwise transformations is a residual network [18] with pre-activation residual blocks [19]. |
| Researcher Affiliation | Academia | Conor Durkan Artur Bekasov Iain Murray George Papamakarios School of Informatics, University of Edinburgh {conor.durkan, artur.bekasov, i.murray, g.papamakarios}@ed.ac.uk |
| Pseudocode | No | The paper describes the practical implementation steps of the monotonic rational-quadratic coupling transform using numbered points in Section 3.1, but it does not present this information in a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Code is available online at https://github.com/bayesiains/nsf. |
| Open Datasets | Yes | We first evaluate our proposed flows using a selection of datasets from the UCI machine-learning repository [7] and BSDS300 collection of natural images [38]. For our experiments, we use dynamically binarized versions of the MNIST dataset of handwritten digits [33], and the EMNIST dataset variant featuring handwritten letters [5]. We use the CIFAR-10 [31] and downsampled 64x64 Image Net [49, 60] datasets. |
| Dataset Splits | Yes | For validation results which can be used for comparison during model development, see table 6 in appendix B.1. |
| Hardware Specification | Yes | Each GPU experiment was run on a single NVIDIA GeForce GTX 1080 Ti with 11GB of RAM. |
| Software Dependencies | Yes | All experiments are run using PyTorch 1.1 with CUDA 10.0 and cuDNN 7.4.1. |
| Experiment Setup | Yes | Unless otherwise stated, we use a constant learning rate of 10^-3, batch size 200, 10 flow steps, K = 8 bins for the splines and a tail bound B = 3. We train each model for 500 epochs for UCI datasets, and 200 epochs for MNIST and EMNIST datasets. |