PredRNN: recurrent neural networks for predictive learning using spatiotemporal LSTMs
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- Neural Information Processing Systems
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doi.org/w54303044 →Countries where authors are citing PredRNN: recurrent neural networks for predictive learning using spatiotemporal LSTMs
This map shows the geographic impact of PredRNN: recurrent neural networks for predictive learning using spatiotemporal LSTMs. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by PredRNN: recurrent neural networks for predictive learning using spatiotemporal LSTMs with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites PredRNN: recurrent neural networks for predictive learning using spatiotemporal LSTMs more than expected).
Fields of papers citing PredRNN: recurrent neural networks for predictive learning using spatiotemporal LSTMs
This network shows the impact of PredRNN: recurrent neural networks for predictive learning using spatiotemporal LSTMs. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the PredRNN: recurrent neural networks for predictive learning using spatiotemporal LSTMs.
About PredRNN: recurrent neural networks for predictive learning using spatiotemporal LSTMs
This paper, published in 2017, received 418 indexed citations . Written by Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao and Philip S. Yu covering the research area of Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Atmospheric Science (163 citations), Global and Planetary Change (126 citations), Computer Vision and Pattern Recognition (119 citations), Artificial Intelligence (64 citations) and Environmental Engineering (58 citations). Published in Neural Information Processing Systems.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w54303044.