Methods › Computer Vision › Generative Models › Hierarchical VAE › Papers where code ran, page 1
Hierarchical Variational Autoencoder
Hierarchical VAE
Papers archive 2025-07-28
archive papers tagged: 25 · with a code link: 16 · where Syntology ran a sample: 8 (6 with a run with no instrument failure, 2 where every run was a failure of Syntology's instrument) Syntology
Show: all tagged papersonly where code ran (8 of 25 tagged: 6 with a run with no instrument failure, 2 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 8 of the 8 tagged papers where Syntology ran at least one harvested sample (6 with a run with no instrument failure, 2 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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Hierarchical VAE with a Diffusion-based VampPrior 2 Dec 2024 · 1 repository · arXiv:2412.01373Syntology official (archive's flag): 1 ran · 1 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; 1 where Syntology's instrument failed) · 1 unverified (of 2 harvested samples) · 2 pointer-only (licence)
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Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction 18 Aug 2023 · 1 repository · arXiv:2309.00073Syntology official (archive's flag): 11 ran · 11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified (of 14 harvested samples) · 14 pointer-only (licence)
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QARV: Quantization-Aware ResNet VAE for Lossy Image Compression 16 Feb 2023 · 2 repositories · arXiv:2302.08899Syntology official: no sample here; runs from other or unrecorded repositories · 1 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; 1 where Syntology's instrument failed) · 0 unverified (of 1 harvested sample) · 1 pointer-only (licence)
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Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo 9 Feb 2022 · 1 repository · arXiv:2202.04599Syntology official (archive's flag): 3 ran · 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) · 3 unverified (of 6 harvested samples)
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SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data 29 Mar 2021 · 2 repositories · arXiv:2103.15619Syntology official (archive's flag): 17 ran · 17 ran (of which 7 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 8 where Syntology's instrument failed) · 10 unverified (of 27 harvested samples)
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Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images 20 Nov 2020 · 8 repositories · arXiv:2011.10650Syntology official (archive's flag): 4 ran · 8 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 1 unverified (of 9 harvested samples) · 4 pointer-only (licence)
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NVAE: A Deep Hierarchical Variational Autoencoder 8 Jul 2020 · 10 repositories · arXiv:2007.03898Syntology official (archive's flag): 9 ran · 26 ran (of which 15 constructed an object rather than computing a result; 21 with no instrument failure: 3 honoured, 0 violated, 18 with no contract checked; 5 where Syntology's instrument failed) · 15 unverified (of 41 harvested samples) · 23 pointer-only (licence)
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Ladder Variational Autoencoders 6 Feb 2016 · 5 repositories · arXiv:1602.02282Syntology community repositories only · 7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified (of 9 harvested samples) · 2 pointer-only (licence)