Methods › General › Output Functions › Mixture of Logistic Distributions

Mixture of Logistic Distributions

170 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Mixture of Logistic Distributions (MoL) is a type of output function, and an alternative to a softmax layer. Discretized logistic mixture likelihood is used in PixelCNN++ and WaveNet to predict discrete values.

Image Credit: Hao Gao

Papers archive 2025-07-28

30 shown of 170, 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.

Tasks archive 2025-07-28

20 shown of 121 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Speech Synthesis54
Text to Speech50
text-to-speech50
Decoder18
Text-To-Speech Synthesis15
Voice Conversion12
Audio Synthesis10
Audio Generation8
GPU8
Time Series7
Deep Learning6
Speech Enhancement6
Generative Adversarial Network5
Speech Recognition5
Time Series Analysis5
Transfer Learning5
Translation5
CPU4
Data Augmentation4
Graph Neural Network4

Usage over time archive 2025-07-28

Papers per year tagged with Mixture of Logistic Distributions: 2016 to 2025, peak 37 37 0 2016: 2 papers 2016 2017: 10 papers 2017 2018: 26 papers 2018 2019: 37 papers 2019 2020: 32 papers 2020 2021: 24 papers 2021 2022: 13 papers 2022 2023: 9 papers 2023 2024: 13 papers 2024 2025: 4 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (170 dated). Bars are counts, not a trend claim.

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

Output Functions

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