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Gumbel Softmax

52 papers tagged archive 2025-07-28

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

Gumbel-Softmax is a continuous distribution that has the property that it can be smoothly annealed into a categorical distribution, and whose parameter gradients can be easily computed via the reparameterization trick.

Source: Categorical Reparameterization with Gumbel-SoftmaxSee Code · ericjang/gumbel-softmax

Papers archive 2025-07-28

30 shown of 52, 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 87 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
Neural Architecture Search16
Clustering6
GPU4
General Classification4
Image Classification4
Speech Recognition4
Time Series4
reinforcement-learning4
Quantization3
Recommendation Systems3
Reinforcement Learning (RL)3
Transfer Learning3
image-classification3
Benchmarking2
Diversity2
Evolutionary Algorithms2
Graph Clustering2
Meta-Learning2
Model Compression2
Multi-agent Reinforcement Learning2

Usage over time archive 2025-07-28

Papers per year tagged with Gumbel Softmax: 2016 to 2025, peak 10 10 0 2016: 1 paper 2016 2017: 0 papers 2017 2018: 5 papers 2018 2019: 3 papers 2019 2020: 7 papers 2020 2021: 10 papers 2021 2022: 10 papers 2022 2023: 6 papers 2023 2024: 8 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (52 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

Distributions

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