Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
Authors: Nikolaos Karalias, Joshua Robinson, Andreas Loukas, Stefanie Jegelka
NeurIPS 2022 | Venue PDF | 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 EMAIL Joshua Robinson MIT CSAIL EMAIL Andreas Loukas Prescient Design, Genentech, Roche EMAIL Stefanie Jegelka MIT CSAIL EMAIL |
| 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. |