L-BFGS
A quasi-Newton optimization algorithm that approximates the inverse of the Hessian matrix using gradient differences from the most recent steps. It has low memory requirements and is suitable for large-scale optimization problems.
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ARA encodes the desired modification attributes through an objective function and uses the L-BFGS optimizer to modify matrices one by one, with no rank restriction.
Deep learning parameter dimensions are extremely high, making exact second-order methods infeasible; in practice, quasi-Newton methods (such as L-BFGS) or adaptive methods (such as Adam) are mostly used to exploit second-order information.
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ARA encodes the desired modification attributes through an objective function and uses the L-BFGS optimizer to modify matrices one by one, with no rank restriction.
50%UnverifiedDeep learning parameter dimensions are extremely high, making exact second-order methods infeasible; in practice, quasi-Newton methods (such as L-BFGS) or adaptive methods (such as Adam) are mostly used to exploit second-order information.
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