Non Differentiable

Non-differentiable functions pose significant challenges in machine learning, hindering the application of gradient-based optimization methods crucial for training many models. Current research focuses on developing techniques to handle these functions, including novel gradient estimation methods like reparameterization and smoothing, and alternative optimization strategies such as reinforcement learning. These advancements are improving the performance and applicability of machine learning models in diverse areas, such as music generation, continual learning, and explainable AI, where non-differentiable components are common. The development of robust and efficient methods for handling non-differentiability is thus vital for expanding the capabilities of machine learning.