Methods › General › Dimensionality Reduction › Variational Inference › Papers where code ran, page 2
Variational Inference
Papers archive 2025-07-28
archive papers tagged: 846 · with a code link: 337 · where Syntology ran a sample: 110 (88 with a run with no instrument failure, 22 where every run was a failure of Syntology's instrument) Syntology
Show: all tagged papersonly where code ran (110 of 846 tagged: 88 with a run with no instrument failure, 22 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 2 of 2: papers 101 to 110 of the 110 tagged papers where Syntology ran at least one harvested sample (88 with a run with no instrument failure, 22 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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Variational Causal Networks: Approximate Bayesian Inference over Causal Structures 14 Jun 2021 · 1 repository · arXiv:2106.07635Syntology official (archive's flag): 3 ran · 3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified (of 3 harvested samples)
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Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features 10 Jun 2021 · 1 repository · arXiv:2106.05960Syntology official (archive's flag): 2 ran · 2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified (of 3 harvested samples)
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Probabilistic task modelling for meta-learning 9 Jun 2021 · 1 repository · arXiv:2106.04802Syntology official (archive's flag): 4 ran · 4 ran (of which 0 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) · 1 unverified (of 5 harvested samples)
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Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation 5 Jun 2021 · 1 repository · arXiv:2106.02960Syntology 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) · 0 unverified; every one of the 4 samples that ran constructed an object rather than computing a result (of 4 harvested samples)
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A Differentiable Point Process with Its Application to Spiking Neural Networks 2 Jun 2021 · 1 repository · arXiv:2106.00901Syntology official (archive's flag): 8 ran · 8 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 7 unverified (of 15 harvested samples)
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Consistency Regularization for Variational Auto-Encoders 31 May 2021 · 1 repository · arXiv:2105.14859Syntology official (archive's flag): 2 ran · 2 ran (of which 0 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) · 1 unverified (of 3 harvested samples)
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DiBS: Differentiable Bayesian Structure Learning 25 May 2021 · 2 repositories · arXiv:2105.11839Syntology official (archive's flag): 3 ran · 3 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; 3 where Syntology's instrument failed) · 1 unverified (of 4 harvested samples)
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What Are Bayesian Neural Network Posteriors Really Like? 29 Apr 2021 · 3 repositories · arXiv:2104.14421Syntology community repositories only · 3 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; 2 where Syntology's instrument failed) · 0 unverified (of 3 harvested samples) · 3 pointer-only (licence)
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Differentiable Particle Filtering via Entropy-Regularized Optimal Transport 15 Feb 2021 · 1 repository · arXiv:2102.07850Syntology 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)
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Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations 12 Feb 2021 · 2 repositories · arXiv:2102.06559Syntology official (archive's flag): 3 ran · 3 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; 3 where Syntology's instrument failed) · 3 unverified (of 6 harvested samples) · 3 pointer-only (licence)