Explicit Constraint

Explicit constraints in machine learning and deep learning aim to incorporate prior knowledge or limitations directly into the model's optimization process, improving performance, safety, and reliability. Current research focuses on developing methods to effectively integrate these constraints into various architectures, including neural differential equations and reinforcement learning models, often employing novel regularization techniques or specialized optimization solvers. This approach is crucial for addressing challenges in areas like AI safety, robotics (e.g., pick-and-place tasks), and scientific modeling where adherence to physical laws or other constraints is paramount. The development of robust and efficient methods for handling explicit constraints is driving progress across numerous fields.