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• W2885487913 abstract "In optimization, the negative gradient of a function denotes the direction of steepest descent. Furthermore, traveling in any direction orthogonal to the gradient maintains the value of the function. In this work, we show that these orthogonal directions that are ignored by gradient descent can be critical in equilibrium problems. Equilibrium problems have drawn heightened attention in machine learning due to the emergence of the Generative Adversarial Network (GAN). We use the framework of Variational Inequalities to analyze popular training algorithms for a fundamental GAN variant: the Wasserstein Linear-Quadratic GAN. We show that the steepest descent direction causes divergence from the equilibrium, and convergence to the equilibrium is achieved through following a particular orthogonal direction. We call this successful technique Crossing-the-Curl, named for its mathematical derivation as well as its intuition: identify the game's axis of rotation and move across space in the direction towards smaller curling." @default.
• W2885487913 created "2018-08-22" @default.
• W2885487913 creator A5047721934 @default.
• W2885487913 creator A5061960274 @default.
• W2885487913 date "2018-08-04" @default.
• W2885487913 modified "2023-09-27" @default.
• W2885487913 title "Global Convergence to the Equilibrium of GANs using Variational Inequalities." @default.
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• W2885487913 hasPublicationYear "2018" @default.
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