Non Linearity

Non-linearity in various machine learning models is a crucial research area focused on understanding and optimizing its impact on model performance and efficiency. Current research investigates the role of non-linear activation functions (like ReLU and GELU) in neural networks, exploring techniques to approximate or reduce their computational cost while maintaining accuracy, particularly in resource-constrained environments like FPGAs. This includes developing methods to mitigate biases introduced by non-linearity and to better understand how different types of non-linearity affect model behavior in diverse applications, such as computer vision, natural language processing, and recommender systems. Improved understanding and control of non-linearity promises to enhance the efficiency, interpretability, and fairness of machine learning models across numerous fields.