Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selection
Authors: Abir De, Soumen Chakrabarti
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on synthetic and real data show that FLEXSUBNET outperforms several baselines. |
| Researcher Affiliation | Academia | Abir De Soumen Chakrabarti Indian Institute of Technology Bombay {abir,soumen}@cse.iitb.ac.in |
| Pseudocode | No | The paper describes the proposed models and methods in detail using mathematical formulations and descriptive text, but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is in https://tinyurl.com/flexsubnet. Code is included as part of supplemental material. |
| Open Datasets | Yes | We use the Amazon baby registry dataset [30] which contains 17 product categories. ... We generate |V |=104 samples, where we draw the feature vector zs for each sample s V uniformly at random, i.e., zs Unif[0, 1]d with d=10. |
| Dataset Splits | Yes | We sample |V |=10000 (set,value) instances as described above and split them into train, dev and test folds of equal size. ... We split S into equal-sized training (Strain), dev (Sdev) and test (Stest) folds. |
| Hardware Specification | No | The paper states, "Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Appendix F in supplemental material." However, Appendix F is not provided in the main paper, so specific hardware details cannot be extracted. |
| Software Dependencies | No | The paper mentions software like BERT and Gumbel-Sinkhorn network but does not provide specific version numbers for these or any other software dependencies used in the experiments. |
| Experiment Setup | Yes | Appendix D provides hyperparameter tuning details for all methods. ... Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Section 5, Appendix D and F in supplemental material. |