Rectangular Flows for Manifold Learning
Authors: Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now compare our methods against the two-step baseline of Brehmer and Cranmer [8], and also study the memory vs. variance trade-off. We use the real NVP [15] architecture for all flows, except we do not use batch normalization [24] as it causes issues with vjp computations. |
| Researcher Affiliation | Collaboration | Anthony L. Caterini University of Oxford & Layer 6 AI anthony@layer6.ai Gabriel Loaiza-Ganem Layer 6 AI gabriel@layer6.ai Geoff Pleiss Columbia University gmp2162@columbia.edu John P. Cunningham Columbia University jpc2181@columbia.edu |
| Pseudocode | No | We include the CG algorithm in Appendix B for completeness. The pseudocode itself is not within the provided text. |
| Open Source Code | Yes | Our code is available at https://github.com/layer6ai-labs/rectangular-flows. |
| Open Datasets | Yes | FMNIST) [57] than to MNIST [36] with a model trained on the former. We now turn our attention to the tabular datasets used by Papamakarios et al. [45] We also compare performance on the CIFAR-10 dataset [33] |
| Dataset Splits | Yes | We use early stopping with our FID-like score across all models. We also used likelihood annealing, with all experimental details again given in Appendix G. |
| Hardware Specification | No | The provided text does not contain specific details about the hardware used (e.g., GPU/CPU models, memory amounts). |
| Software Dependencies | No | The paper mentions "Py Torch [47] library" (Section 4.3) but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | We include a detailed explanation of this phenomenon in Appendix E, along with all experimental details in Appendix G. We also used likelihood annealing, with all experimental details again given in Appendix G. |