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• W2592457170 abstract "This paper presents OptNet, a network architecture that integrates optimization problems (here, specifically in the form of quadratic programs) as individual layers in larger end-to-end trainable deep networks. These layers encode constraints and complex dependencies between the hidden states that traditional convolutional and fully-connected layers often cannot capture. In this paper, we explore the foundations for such an architecture: we show how techniques from sensitivity analysis, bilevel optimization, and implicit differentiation can be used to exactly differentiate through these layers and with respect to layer parameters; we develop a highly efficient solver for these layers that exploits fast GPU-based batch solves within a primal-dual interior point method, and which provides backpropagation gradients with virtually no additional cost on top of the solve; and we highlight the application of these approaches in several problems. In one notable example, we show that the method is capable of learning to play mini-Sudoku (4x4) given just input and output games, with no a priori information about the rules of the game; this highlights the ability of our architecture to learn hard constraints better than other neural architectures." @default.
• W2592457170 created "2017-03-16" @default.
• W2592457170 creator A5030666794 @default.
• W2592457170 creator A5075035644 @default.
• W2592457170 date "2017-03-01" @default.
• W2592457170 modified "2023-09-27" @default.
• W2592457170 title "OptNet: Differentiable Optimization as a Layer in Neural Networks" @default.
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• W2592457170 hasPublicationYear "2017" @default.
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