Methods › General › Probability distribution representation › kdm
Kernel Density Matrices
kdm
Introduced by Fabio A. González et al. in Kernel Density Matrices for Probabilistic Deep Learning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Kernel density matrices provide a simpler yet effective mechanism for representing joint probability distributions of both continuous and discrete random variables. This abstraction allows the construction of differentiable models for density estimation, inference, and sampling, and enables their integration into end-to-end deep neural models.
Papers archive 2025-07-28
5 shown of 5, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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One-Step Offline Distillation of Diffusion-based Models via Koopman Modeling 19 May 2025 · 1 repository · arXiv:2505.13358
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Kernel Density Machines 30 Apr 2025 · 0 repositories · arXiv:2504.21419
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Key-point Guided Deformable Image Manipulation Using Diffusion Model 16 Jan 2024 · 0 repositories · arXiv:2401.08178
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DisCover: Disentangled Music Representation Learning for Cover Song Identification 19 Jul 2023 · 0 repositories · arXiv:2307.09775
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Kernel Density Matrices for Probabilistic Deep Learning 26 May 2023 · 2 repositories · arXiv:2305.18204
Tasks archive 2025-07-28
19 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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