Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
Authors: Rob Cornish, Anthony Caterini, George Deligiannidis, Arnaud Doucet
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated the performance of CIFs on several problems of varying difficulty, including synthetic 2-D data, several tabular datasets, and three image datasets. |
| Researcher Affiliation | Academia | 1University of Oxford, Oxford, United Kingdom 2The Alan Turing Institute, London, United Kingdom. Correspondence to: Rob Cornish <rcornish@robots.ox.ac.uk>. |
| Pseudocode | Yes | Algorithm 1 Unbiased estimation of L(x) |
| Open Source Code | Yes | See github. com/jrmcornish/cif for our code. |
| Open Datasets | Yes | We tested the performance of CIFs on the tabular datasets used by Papamakarios et al. (2017). For each dataset, we trained 10 and 100-layer baseline fully connected Res Flows, and corresponding 10-layer CIF-Res Flows... We also considered CIFs applied to the MNIST (Le Cun, 1998), Fashion-MNIST (Xiao et al., 2017), and CIFAR-10 (Krizhevsky & Hinton, 2009) datasets. |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide details about a validation dataset split or its use. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names, framework versions) needed to replicate the experiment. |
| Experiment Setup | No | Full experimental details, including additional 2-D figures along the lines of Figure 1, are in Section C of the Supplement. |