<ul><li>Bi-level optimization has become crucial for hierarchical machine learning problems, but traditional gradient-based algorithms are not suitable for large-scale applications.</li><li>A new approach called FG2U (Forward Gradient Unrolling with Forward Fradient) is introduced, which provides more accurate gradient estimates and supports parallel computing.</li><li>FG2U can be used in different stages of the training process and is easily implemented in deep learning frameworks.</li><li>Extensive evaluations demonstrate the superior performance of FG2U in diverse large-scale bi-level optimization tasks.</li></ul>

Memory-Efficient Gradient Unrolling for Large-Scale Bi-level Optimization

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