Deep Equilibrium Model

Deep Equilibrium Models (DEQs) are a class of implicit neural networks that represent the output as the fixed point of a single, potentially infinitely deep, layer, offering memory efficiency compared to traditional deep networks. Current research focuses on improving DEQ stability, convergence guarantees, and efficiency through various architectures and algorithms, including those incorporating physics-informed learning, and adapting DEQs for specific applications like inverse problems and differential equation solving. This approach holds significant promise for accelerating computationally intensive tasks in scientific computing and various engineering domains, particularly where memory limitations pose challenges for traditional deep learning methods.