Whitening Convergence Rate of Coupling-based Normalizing Flows
Authors: Felix Draxler, Christoph Schnörr, Ullrich Köthe
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments demonstrate the implications of our theory and point at open questions. |
| Researcher Affiliation | Academia | Felix Draxler Heidelberg University felix.draxler@iwr.uni-heidelberg.de Christoph Schnörr Heidelberg University schnoerr@math.uni-heidelberg.de Ullrich Köthe Heidelberg University ullrich.koethe@iwr.uni-heidelberg.de |
| Pseudocode | No | The paper contains mathematical equations and descriptions of processes, but no explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | The code and the generated data and models can be found at: https://github.com/fdraxler/whiten-nf |
| Open Datasets | Yes | In an experiment, we fit a set of Glow [6] coupling flows of increasing depths to the EMNIST digit dataset [38] using maximum likelihood loss and measure the capability of each flow in decreasing G and S (Details in Appendix A.1). |
| Dataset Splits | No | The paper mentions training and testing on splits of the dataset but does not explicitly specify a validation split or its size/usage. |
| Hardware Specification | Yes | All experiments were run on a single NVIDIA RTX 3090 GPU. |
| Software Dependencies | Yes | The code is written in Python 3.9 using PyTorch 1.10. |
| Experiment Setup | Yes | The model is trained for 200 epochs using the Adam optimizer [45] with a learning rate of 0.0001, a batch size of 512, and an L2 penalty of 10^-5. We use a single affine coupling layer per block followed by a fixed permutation in the rotation layer. |