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 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 jinsungyoon@google.com Yao Zhang University of Cambridge yz555@cam.ac.uk James Jordon University of Oxford james.jordon@wolfson.ox.ac.uk Mihaela van der Schaar University of Cambridge UCLA, Alan Turing Institute mv472@cam.ac.uk |
| 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). |