Fourier Learning with Cyclical Data
Authors: Yingxiang Yang, Zhihan Xiong, Tianyi Liu, Taiqing Wang, Chong Wang
ICML 2022 | Conference PDF | Archive PDF | Plain Text | 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. |