Shape Constraints for Set Functions
Authors: Andrew Cotter, Maya Gupta, Heinrich Jiang, Erez Louidor, James Muller, Tamann Narayan, Serena Wang, Tao Zhu
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world data show the achieved accuracy is similar to deep sets or deep neural networks, but provides guarantees on the model behavior, which makes it easier to explain and debug. |
| Researcher Affiliation | Industry | Google AI, Mountain View, CA. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., links or explicit statements) to open-source code for the described methodology. |
| Open Datasets | Yes | For the Kaggle puzzles dataset, the goal is to predict the number of sales of each of 199 puzzles over a six month window given its set of customer reviews at the beginning of the six month period. The Kaggle Kickstarter dataset has N = 331,034 examples of Kickstarter campaign titles that are labeled as succeeded or failed. The Kaggle recipes dataset consists of N = 39,774 recipes from 20 cuisines. |
| Dataset Splits | Yes | We split those examples randomly 70/10/20 to form a train/validation/test set, then split the train data 90/10 to train the SFE and the set function on separate train data; the DNN was trained on all 100% of the training data. The dataset was randomly split 70/10/20 into a train/validation/test set, and then the train set was randomly split 90/10 into SFE train set and set function train set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running its experiments, such as GPU/CPU models or cloud instance types. |
| Software Dependencies | No | The paper mentions a "TensorFlow implementation" but does not specify a version number for TensorFlow or any other software dependencies. |
| Experiment Setup | No | The paper states, "All hyperparameters were validated: see Appendix J for details." This indicates that details exist but are not presented in the main text. |