Transfer Knowledge between Cities
By Ying Wei, Yu Zheng, Qiang Yang. 2016
157 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
Citations in the corpus
- A Comprehensive Survey on Transfer Learning (2019, background): “[205] proposed a flexible multimodal transfer learning approach that transfers knowledge from a city that have sufficient multimodel data and labels to the target city to alleviate the data sparsity problem.”
- CityTransfer (2018, methodology): “As explored in other urban computing studies [8, 37], local grid features extracted from multi-source datasets are redundant and noisy.”
- Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions (2021, methodology): “Although recent approaches have been proposed ( [70], [91], [123], [124]”
- Transfer Learning via Learning to Transfer (2018, background): “Copyright 2018 by the author(s). image classification (Long et al., 2015), sentiment classification (Blitzer et al., 2006), dialog systems (Mo et al., 2016), and urban computing (Wei et al., 2016).”
- Personalizing a Dialogue System With Transfer Reinforcement Learning (2016, unknown): “Transfer learning [21, 14, 19, 20, 23] has been applied to other tasks in dialogue systems.”
- Deep learning in environmental remote sensing: Achievements and challenges (2020, background): “An example of knowledge transfer from a city with su ffi cient samples to other similar cities with the small sample size problem has been presented in the study of Wei et al. (2016).”
- Towards more Reliable Transfer Learning (2018, background): “Plenty of research has been done on the single-source setting [25,22,35], and it has been shown that transfer learning has a wide range of applications in areas such as sentiment analysis [3,21], computer vision [15], cross-lingual natural language processing [17], and urban computing [32].”