Developing Training Procedures for Piecewise-linear Spline Activation Functions in Neural Networks

• URL: http://arxiv.org/abs/2509.18161v1
• Date: Wed, 17 Sep 2025 03:51:16 GMT
• Title: Developing Training Procedures for Piecewise-linear Spline Activation Functions in Neural Networks
• Authors: William H Patty,
• Abstract summary: I present and compare 9 training methodologies to explore dual-optimization dynamics in neural networks.<n>Experiments realize up to 94% lower end model error rates in FNNs and 51% lower rates in CNNs compared to traditional ReLU-based models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Activation functions in neural networks are typically selected from a set of empirically validated, commonly used static functions such as ReLU, tanh, or sigmoid. However, by optimizing the shapes of a network's activation functions, we can train models that are more parameter-efficient and accurate by assigning more optimal activations to the neurons. In this paper, I present and compare 9 training methodologies to explore dual-optimization dynamics in neural networks with parameterized linear B-spline activation functions. The experiments realize up to 94% lower end model error rates in FNNs and 51% lower rates in CNNs compared to traditional ReLU-based models. These gains come at the cost of additional development and training complexity as well as end model latency.

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