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..
Fourier Learning with Cyclical Data
Authors: Yingxiang Yang, Zhihan Xiong, Tianyi Liu, Taiqing Wang, Chong Wang
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate Fourier learning s better performance with extensive experiments on synthetic and public datasets, as well as on a large-scale recommender system that is updated in real-time, and trained with tens of millions of samples per day. 5. Numerical Simulations In this section, we numerically demonstrate the superiority of Fourier learning over the prior state-of-the-arts on synthetic and public datasets. 6. Fourier Learning in Recommender Systems In this section, we report the performance of Fourier learning implemented on a conversion rate (CVR) prediction model in an industrial recommender system. |
| Researcher Affiliation | Collaboration | 1Byte Dance Inc 2Paul G. Allen School of Computer Science & Engineering, University of Washington, WA. |
| Pseudocode | Yes | Algorithm 1 Streaming-SGD for F-MLP |
| Open Source Code | Yes | The source code and logs used to generate the reported experiments can be found at https://github.com/ Yangyx891121/Fourier-Learning. |
| Open Datasets | Yes | We classified the sentiment of tweets using a bag-of-words model over the Sentiment140 Twitter (Go et al., 2009) dataset. We demonstrate Fourier learning s better performance with extensive experiments on synthetic and public datasets, as well as on a large-scale recommender system that is updated in real-time, and trained with tens of millions of samples per day. |
| Dataset Splits | No | The paper uses concepts like 'streaming data' and 'online learning' where data arrives sequentially for training, but it does not provide specific percentages or counts for train/validation/test splits. |
| Hardware Specification | No | The paper states 'We trained the model on a distributed machine learning platform with 3,600 CPU cores', but it does not specify exact CPU models, GPU models, or other detailed hardware components used for experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | Yes | We conducted the experiment over 15 days of data, and plotted the results for lr = 0.1 in Figure 5 (see Appendix I for more results). Under the aforementioned design, we set the base frequency of the Fourier learning model to 2.4192MHz, or (28 days) 1, and set N = 28 4 in (7), providing a frequency band of up to (6 hours) 1. The coefficient ΞΎ is set to 0.5. |