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..
CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions
Authors: Isha Puri, Amit Dhurandhar, Tejaswini Pedapati, Karthikeyan Shanmugam, Dennis Wei, Kush R. Varshney
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On various regression and time-series forecasting tasks, we show that CoFRNets provide competitive performance when compared to other interpretable models and deep neural networks. |
| Researcher Affiliation | Academia | Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences |
| Pseudocode | No | The paper describes the architecture and its components using mathematical formulations and textual descriptions but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The code and data for CoFRNets will be made publicly available upon acceptance. |
| Open Datasets | Yes | We use a total of eight benchmark datasets for regression and time-series forecasting, including six UCI regression datasets (Concrete, Energy, Power Plant, Protein, Yacht, Wine) and two time-series datasets (Pollution, Electricity). |
| Dataset Splits | Yes | For all datasets, we train the models using 80% of the data for training, 10% for validation, and 10% for testing. |
| Hardware Specification | No | The paper mentions software details like PyTorch and Adam optimizer but does not provide any specific hardware details such as GPU/CPU models or other computing specifications used for experiments. |
| Software Dependencies | No | The paper states 'CoFRNets are implemented in PyTorch using the Adam optimizer' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For all datasets, we train the models using 80% of the data for training, 10% for validation, and 10% for testing. The models are trained for 200 epochs with a batch size of 64. The learning rate is initialized to 0.001 and decayed by a factor of 0.1 every 50 epochs. We use a weight decay of 0.0001 and a dropout rate of 0.1. |