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 [1].
Hierarchical Lattice Layer for Partially Monotone Neural Networks
Authors: Hiroki Yanagisawa, Kohei Miyaguchi, Takayuki Katsuki
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that HLL did not sacri๏ฌce its prediction performance on real datasets compared with the lattice layer. |
| Researcher Affiliation | Industry | Hiroki Yanagisawa IBM Research Tokyo IBM Japan, Ltd. Tokyo, Japan EMAIL Kohei Miyaguchi IBM Research Tokyo IBM Japan, Ltd. Tokyo, Japan EMAIL Takayuki Katsuki IBM Research Tokyo IBM Japan, Ltd. Tokyo, Japan EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We used Python 3.8.12 and Py Torch 1.8.1 to implement MLP and HLL, and the implementation of HLL is available at https://github.com/IBM/pmlayer. |
| Open Datasets | Yes | We used 12 real datasets taken from the UCI Machine Learning Repository [Dua and Graff, 2017] in our experiments. |
| Dataset Splits | Yes | In our experiments, we split the data points into training, validation, and test data points. For the unsplit datasets, we divided the data points into training (60%), validation (20%), and test (20%). For the datasets that were already split into training and test datasets, we further divided the data points in the training dataset into training (80%) and validation (20%) and kept the test dataset unchanged. |
| Hardware Specification | Yes | All our experiments were conducted on a virtual machine with an Intel Xeon CPU (3.30 GHz) processor without any GPU and 64 GB of memory running Red Hat Enterprise Linux Server 7.6. |
| Software Dependencies | Yes | We used Python 3.8.12 and Py Torch 1.8.1 to implement MLP and HLL... we used Tensor Flow 2.3.0 and Tensor Flow Lattice 2.0.10. |
| Experiment Setup | Yes | Regarding the hyperparameters, we chose the best hyperparameters for each combination of a neural network and a dataset; the learning rate was chosen from {1, 10 1, 10 2, 10 3, 10 4, 10 5}, the batch size was chosen from {8, 16, 32, ..., 4096}, the number of neurons in the hidden layers was chosen from {16, 32, 64, ..., 512} for MLP and HLL, and the hyperparameter r was chosen from {4, 5, 6, 7} for TL RTL. We used the Adam optimizer [Kingma and Ba, 2015]. We trained these neural network models for 1000 epochs (small datasets) or 100 epochs (large datasets) with certain early stopping criteria. |