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cs.LG2019
Composition and decomposition of GANs
Yeu-Chern Harn, Zhenghao Chen, Vladimir Jojic
In this work, we propose a composition/decomposition framework for adversarially training generative models on composed data - data where each sample can be thought of as being con…
cs.LG2016
Degrees of Freedom in Deep Neural Networks
Tianxiang Gao, Vladimir Jojic
In this paper, we explore degrees of freedom in deep sigmoidal neural networks. We show that the degrees of freedom in these models is related to the expected optimism, which is th…