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.
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain
Authors: Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,325 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,669 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 | In experiments, we evaluate the proposed framework in multiple tabular datasets from various application domains, such as genomics and clinical data. VIME exceeds state-of-the-art performance in comparison to the existing baseline methods. |
| Researcher Affiliation | Collaboration | Jinsung Yoon Google Cloud AI, UCLA EMAIL Yao Zhang University of Cambridge EMAIL James Jordon University of Oxford EMAIL Mihaela van der Schaar University of Cambridge UCLA, Alan Turing Institute EMAIL |
| Pseudocode | No | The paper includes block diagrams (Figure 1 and Figure 2) but no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementation of VIME can be found at https://bitbucket.org/mvdschaar/mlforhealthlabpub/src/master/alg/vime/ and at https://github.com/jsyoon0823/VIME. |
| Open Datasets | Yes | To further verify the generalizability and allow for reproducibility of our results, we compare VIME with the benchmarks using three public tabular datasets: MNIST (interpreted as a tabular data with 784 features), UCI Income and UCI Blog. |
| Dataset Splits | No | The paper does not explicitly state the training/test/validation dataset splits. It mentions |
| 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. |
| Experiment Setup | Yes | Implementation details and sensitivity analyses on three hyperparameters (pm, α, β) can be found in the Supplementary Materials (Section 5 & 6). |