Methods › Graphs › Graph Models › DCNN › Papers where code ran, page 1
Diffusion-Convolutional Neural Networks
DCNN
Papers archive 2025-07-28
archive papers tagged: 277 · with a code link: 81 · where Syntology ran a sample: 7 (6 with a run with no instrument failure, 1 where every run was a failure of Syntology's instrument) Syntology
Show: all tagged papersonly where code ran (7 of 277 tagged: 6 with a run with no instrument failure, 1 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 (6 with a run with no instrument failure, 1 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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Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging 12 Mar 2021 · 1 repository · arXiv:2103.07152Syntology 3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 3 samples that ran constructed an object rather than computing a result (of 4 harvested samples) · 4 pointer-only (licence)
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Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion 10 Mar 2020 · 1 repository · arXiv:2003.04490Syntology official (archive's flag): 1 ran · 1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified (of 1 harvested sample) · 1 pointer-only (licence)
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Coupled Ensembles of Neural Networks 18 Sep 2017 · 2 repositories · arXiv:1709.06053Syntology official (archive's flag): 1 ran · 1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified (of 1 harvested sample) · 1 pointer-only (licence)
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Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms 6 Mar 2017 · 3 repositories · arXiv:1703.01789Syntology 2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified (of 3 harvested samples) · 2 pointer-only (licence)
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs 2 Jun 2016 · 47 repositories · arXiv:1606.00915Syntology 43 ran (of which 12 constructed an object rather than computing a result; 39 with no instrument failure: 1 honoured, 0 violated, 38 with no contract checked; 4 where Syntology's instrument failed) · 20 unverified (of 63 harvested samples) · 18 pointer-only (licence)
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Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis 18 Jan 2016 · 7 repositories · arXiv:1601.04589Syntology community repositories only · 7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 11 unverified (of 18 harvested samples)
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Diffusion-Convolutional Neural Networks 6 Nov 2015 · 3 repositories · arXiv:1511.02136Syntology 3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified (of 3 harvested samples)