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.
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
Authors: Rob Cornish, Anthony Caterini, George Deligiannidis, Arnaud Doucet
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 20,708 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,248 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 | 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 <EMAIL>. |
| 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. |