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Adaptive Activation
AdAct
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
ADACT: LEARNING TO OPTIMIZE ACTIVATION FUNCTION CHOICE THROUGH ADAPTIVE ACTIVATION MODULES
This paper presents an innovative approach to enhancing neural network performance through the development and implementation of an adaptive activation function, termed adaptive activation (AdAct). AdAct amalgamates various wellestablished and novel activation functions into a single, learnable framework, allowing dynamic adaptation to specific network layers’ needs. We explore the effectiveness of ReLU and its variants, including ELU, LReLU, PReLU, RReLU, and more recent functions like Swish and Mish, integrating them into the AdAct function. Employing ConvNet variants across FMNIST, CIFAR10, SVHN and FER datasets, our study empirically assesses each function’s contribution and demonstrates AdAct’s potential in optimizing neural networks, especially in selecting optimal activation functions for diverse tasks.
Papers archive 2025-07-28
1 shown of 1, 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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Large Transformers are Better EEG Learners 20 Aug 2023 · 0 repositories · arXiv:2308.11654
Tasks archive 2025-07-28
7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Activity Recognition | 1 |
| EEG | 1 |
| Eeg Decoding | 1 |
| Electroencephalogram (EEG) | 1 |
| Human Activity Recognition | 1 |
| Time Series | 1 |
| Transfer Learning | 1 |
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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