Beyond Backprop: Online Alternating Minimization with Auxiliary Variables

24 Jun 2018Anna ChoromanskaBenjamin CowenSadhana KumaravelRonny LussMattia RigottiIrina RishBrian KingsburyPaolo DiAchilleViatcheslav GurevRavi TejwaniDjallel Bouneffouf

Despite significant recent advances in deep neural networks, training them remains a challenge due to the highly non-convex nature of the objective function. State-of-the-art methods rely on error backpropagation, which suffers from several well-known issues, such as vanishing and exploding gradients, inability to handle non-differentiable nonlinearities and to parallelize weight-updates across layers, and biological implausibility... (read more)

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