Input Optimization

Input optimization focuses on finding optimal input values to a model, maximizing or minimizing a desired objective function. Current research explores efficient algorithms, including neural network approximations and novel regularization techniques, to address this computationally expensive task, particularly within deep learning architectures like deep equilibrium models. These advancements are improving performance in diverse applications such as generative modeling, adversarial learning, and robust machine learning against out-of-distribution data, leading to more efficient and resilient AI systems.