Efficient Dictionary Learning with Gradient Descent

ICLR 2019 Dar GilboaSam BuchananJohn Wright

Randomly initialized first-order optimization algorithms are the method of choice for solving many high-dimensional nonconvex problems in machine learning, yet general theoretical guarantees cannot rule out convergence to critical points of poor objective value. For some highly structured nonconvex problems however, the success of gradient descent can be understood by studying the geometry of the objective... (read more)

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