Methods › Sequential › Recurrent Neural Networks › ConvLSTM › Papers where code ran, page 1
ConvLSTM
Papers archive 2025-07-28
archive papers tagged: 145 · with a code link: 62 · where Syntology ran a sample: 7 (7 with a run with no instrument failure, 0 where every run was a failure of Syntology's instrument) Syntology
Show: all tagged papersonly where code ran (7 of 145 tagged: 7 with a run with no instrument failure, 0 where every run was a failure of Syntology's instrument)
Syntology We ran code from the paper's repository; we did not isolate this method inside it.
Page 1 of 1: papers 1 to 7 of the 7 tagged papers where Syntology ran at least one harvested sample (7 with a run with no instrument failure, 0 where every run was a failure of Syntology's instrument), newest first by the archive's date (ties by arXiv id). This is a filter on Syntology's record ordered by date only, not a ranking; a run is not a correctness claim. A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.
Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code, as “N ran (of which C constructed an object rather than computing a result; K with no instrument failure: H honoured, V violated, P with no contract checked; I where Syntology's instrument failed) · U unverified”; the instrument figure counts failures of Syntology's instrument, not of the code. It is per sample and not a correctness claim. When the archive marks a repository official for the paper, the line starts with that repository's state (the archive's flag, not a verdict on who wrote the code; “community repositories only” when every sample that ran came from a community repository, “official: no sample here; runs from other or unrecorded repositories” when some came from a repository the paper names or has in its text, or from none recorded); hover it for the repositories the samples that ran came from.
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Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics 19 Jul 2024 · 1 repository · arXiv:2407.14129Syntology official (archive's flag): 1 ran · 1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified (of 2 harvested samples)
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SwinLSTM:Improving Spatiotemporal Prediction Accuracy using Swin Transformer and LSTM 19 Aug 2023 · 1 repository · arXiv:2308.09891Syntology official (archive's flag): 4 ran · 4 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified; every one of the 4 samples that ran constructed an object rather than computing a result (of 9 harvested samples)
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Temporal Modulation Network for Controllable Space-Time Video Super-Resolution 21 Apr 2021 · 1 repository · arXiv:2104.10642Syntology official (archive's flag): 1 ran · 1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified (of 6 harvested samples)
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Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution 26 Feb 2020 · 3 repositories · arXiv:2002.11616Syntology community repositories only · 7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 2 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified (of 8 harvested samples)
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Convolutional Tensor-Train LSTM for Spatio-temporal Learning 21 Feb 2020 · 2 repositories · arXiv:2002.09131Syntology official (archive's flag): 1 ran · 2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result (of 2 harvested samples) · 2 pointer-only (licence)
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Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model 12 Jun 2017 · 4 repositories · arXiv:1706.03458Syntology 9 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 8 where Syntology's instrument failed) · 1 unverified (of 10 harvested samples) · 5 pointer-only (licence)
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Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting 13 Jun 2015 · 23 repositories · arXiv:1506.04214Syntology 2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified (of 3 harvested samples) · 2 pointer-only (licence)