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Adaptive Richard's Curve Weighted Activation

ARiA

30 papers tagged archive 2025-07-28

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

This work introduces a novel activation unit that can be efficiently employed in deep neural nets (DNNs) and performs significantly better than the traditional Rectified Linear Units (ReLU). The function developed is a two parameter version of the specialized Richard's Curve and we call it Adaptive Richard's Curve weighted Activation (ARiA). This function is non-monotonous, analogous to the newly introduced Swish, however allows a precise control over its non-monotonous convexity by varying the hyper-parameters. We first demonstrate the mathematical significance of the two parameter ARiA followed by its application to benchmark problems such as MNIST, CIFAR-10 and CIFAR-100, where we compare the performance with ReLU and Swish units. Our results illustrate a significantly superior performance on all these datasets, making ARiA a potential replacement for ReLU and other activations in DNNs.

Source: ARiA: Utilizing Richard's Curve for Controlling the...

Papers archive 2025-07-28

30 shown of 30, 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 48 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
Benchmarking4
Object3
Object Detection3
object-detection3
3D Object Detection2
Decoder2
Language Modeling2
Language Modelling2
Large Language Model2
NeRF2
Pose Estimation2
3D Reconstruction1
3D geometry1
Action Anticipation1
Action Recognition1
Contact Detection1
Diversity1
Event-based vision1
Federated Learning1
Gaze Estimation1

Usage over time archive 2025-07-28

Papers per year tagged with ARiA: 2018 to 2025, peak 13 13 0 2018: 1 paper 2018 2019: 0 papers 2019 2020: 0 papers 2020 2021: 0 papers 2021 2022: 0 papers 2022 2023: 6 papers 2023 2024: 13 papers 2024 2025: 10 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (30 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

Activation Functions

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