SmartMixed: A Two-Phase Training Strategy for Adaptive Activation Function Learning in Neural Networks
arXiv:2510.22450v3 Announce Type: replace Abstract: The choice of activation function plays a critical role in neural networks, yet most architectures still rely on fixed, uniform activation functions across all neurons. We introduce SmartMixed, a novel two-phase training strategy that allows networks to learn optimal per-neuron activation functions while preserving computational efficiency at inference. In the first phase, neurons adaptively select from a pool of candidate activation functions (ReLU, Sigmoid, Tanh, Leaky\_ReLU, ELU, SELU) using a differentiable hard mixture mechanism. In the
The continuous drive for performance optimization in neural networks, coupled with advancements in computational techniques, makes this an opportune time for developing more adaptive training strategies.
Adaptive activation functions could significantly enhance neural network performance and efficiency, potentially reducing computational costs and improving model accuracy across various AI applications.
Neural networks could move away from fixed, uniform activation functions towards dynamically learned, neuron-specific functions, leading to more robust and optimized model architectures.
- · AI researchers
- · Deep learning practitioners
- · Cloud computing providers
- · Companies deploying AI at scale
- · Developers relying solely on static activation functions
- · Hardware optimized for less complex activation landscapes
Improved performance and efficiency of neural networks across various tasks.
Reduced computational resource requirements for training and inference, potentially lowering barriers to entry for advanced AI deployment.
Acceleration of research into more complex and adaptive neural network architectures, further pushing the boundaries of AI capabilities.
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Read at arXiv cs.LG