arXiv:2601.06441v2 Announce Type: replace Abstract: Learning activation functions has emerged as a promising direction in deep learning, allowing networks to adapt activation mechanisms to task-specific demands. In this work, we introduce a novel framework that employs the Gumbel-Softmax trick to enable discrete yet differentiable selection among a predefined set of activation functions during training. Our method dynamically learns the optimal activation function independently of the input, thereby enhancing both predictive accuracy and architectural flexibility. Experiments on synthetic data
The ongoing pursuit of higher efficiency and adaptability in deep learning models drives continuous innovation in core architectural components like activation functions.
This development allows AI models to dynamically optimize their internal functions, leading to improved predictive accuracy and broader applicability across diverse tasks.
Deep learning models can now intelligently select optimal activation functions during training, rather than relying on fixed or manually tuned options, enhancing both performance and development flexibility.
- · AI researchers
- · Deep learning framework developers
- · Industries using adaptive AI models
- · Developers relying on manual activation function tuning
- · Less adaptive deep learning architectures
Increased efficiency and performance of deep learning models across multiple applications.
Faster development and deployment cycles for AI solutions as architectural tuning becomes more automated.
Potentially enables more complex and robust AI agents with greater learning capabilities.
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Read at arXiv cs.LG