Exclusive Square Activation

Exclusive square activation functions are gaining traction in deep learning, particularly within the context of private inference, where their computational efficiency significantly reduces latency compared to traditional activation functions like ReLU. Research focuses on mitigating the accuracy loss typically associated with square activations, employing novel architectures like xMLP to achieve performance parity with ReLU-based models. This improved efficiency is crucial for enhancing the practicality of privacy-preserving machine learning applications, while also impacting broader areas like time series analysis through optimized kernel learning methods.