Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
Authors: Nikolaos Karalias, Joshua Robinson, Andreas Loukas, Stefanie Jegelka
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we observe benefits of our extensions for unsupervised neural combinatorial optimization, in particular with high-dimensional representations. and We experiment with SFEs as loss functions in neural network pipelines on discrete objectives arising in combinatorial and vision tasks. |
| Researcher Affiliation | Collaboration | Nikolaos Karalias EPFL nikolaos.karalias@epfl.ch Joshua Robinson MIT CSAIL joshrob@mit.edu Andreas Loukas Prescient Design, Genentech, Roche andreas.loukas@roche.com Stefanie Jegelka MIT CSAIL stefje@csail.mit.edu |
| Pseudocode | No | The paper describes its methods through mathematical formulations and prose, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'see Appendix E for details on data, hardware, and software,' implying that code information might be there, but Appendix E is not provided, and there is no explicit statement or link to an open-source code repository within the available text. |
| Open Datasets | Yes | We use the ENZYMES, PROTEINS, IMDB, MUTAG, and COLLAB datasets from the TUDatasets benchmark (Morris et al., 2020) |
| Dataset Splits | Yes | We use the ENZYMES, PROTEINS, IMDB, MUTAG, and COLLAB datasets from the TUDatasets benchmark (Morris et al., 2020), using a 60/30/10 split for train/test/val. |
| Hardware Specification | No | The paper states, 'see Appendix E for details on data, hardware, and software.' However, Appendix E is not provided within the given text, so specific hardware details are not available. |
| Software Dependencies | No | The paper states, 'see Appendix E for details on data, hardware, and software.' However, Appendix E is not provided within the given text, so specific software dependencies with version numbers are not detailed in the main body. |
| Experiment Setup | No | The paper states, 'Finally, see Appendix F for training and hyper-parameter optimization details'. However, Appendix F is not provided within the given text, so specific experimental setup details like hyperparameters are not available in the main body. |