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Creators/Authors contains: "Sypherd, Tyler"

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  1. ; ; ; (, IEEE International Symposium on Information Theory)
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  2. ; ; ; ; ; (, IEEE Transactions on Information Theory)
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  3. ; ; (, 2021 IEEE Information Theory Workshop (ITW))
    We introduce a tunable GAN, called α-GAN, parameterized by α∈(0, ∞], which interpolates between various f-GANs and Integral Probability Metric based GANs (under constrained discriminator set). We construct α− GAN using a supervised loss function, namely, α− loss, which is a tunable loss function capturing several canonical losses. We show that α− GAN is intimately related to the Arimoto divergence, which was first proposed by Österriecher (1996), and later studied by Liese and Vajda (2006). We posit that the holistic understanding that α− GAN introduces will have practical benefits of addressing both the issues of vanishing gradients and mode collapses. 
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  4. ; ; ; (, 2020 IEEE International Symposium on Information Theory)
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    We analyze the optimization landscape of a recently introduced tunable class of loss functions called α-loss, α ∈ (0, ∞], in the logistic model. This family encapsulates the exponential loss (α = 1/2), the log-loss (α = 1), and the 0-1 loss (α = ∞) and contains compelling properties that enable the practitioner to discern among a host of operating conditions relevant to emerging learning methods. Specifically, we study the evolution of the optimization landscape of α-loss with respect to α using tools drawn from the study of strictly-locally-quasi-convex functions in addition to geometric techniques. We interpret these results in terms of optimization complexity via normalized gradient descent. 
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  5. ; ; ; (, International Symposium on Information Theory)
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