Differentiable Program

Differentiable programming (DP) focuses on designing and optimizing programs whose operations are differentiable, enabling the use of gradient-based methods for training and optimization across the entire program, not just the model. Current research emphasizes integrating DP into various applications, including augmenting large language models with algorithmic capabilities, improving the interpretability of physics-informed neural networks through symbolic regression, and automating machine learning pipelines by making data preprocessing steps differentiable. This approach promises to enhance the efficiency, interpretability, and automation of complex computational tasks, impacting fields ranging from artificial intelligence and scientific computing to engineering and industrial applications.