Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Whitening Convergence Rate of Coupling-based Normalizing Flows
Authors: Felix Draxler, Christoph Schnörr, Ullrich Köthe
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,237 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,049 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL Christoph Schnörr Heidelberg University EMAIL Ullrich Köthe Heidelberg University EMAIL |
| 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. |