Breaking the Gradient: Supervised Learning with Non-Differentiable Loss Functions

The article discusses the challenges of using non-differentiable loss functions in supervised learning models. It explains that while gradient descent is commonly used to optimize differentiable loss functions, it cannot be applied to non-differentiable ones. This limitation can be problematic for certain real-world applications where the loss function may not be differentiable. The author then explores alternative optimization techniques such as genetic and evolutionary algorithms, which do not rely on gradient information. However, these methods have their drawbacks, including slow training times and lack of GPU support. The article also introduces an adaptation of the Actor-Critic method from reinforcement learning to supervised learning. In this approach, a critic network is used to estimate the loss function's value, while an actor network updates its parameters based on the critic's output. This allows for training with non-differentiable loss functions and opens up possibilities for further exploration of alternative optimization techniques inspired by biological processes.