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Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions Wei Jiang 1, Sifan Y ang
Neural Information Processing Systems
Problem (1) has been comprehensively investigated in the literature [Duchi et al., 2011, Kingma and Ba, 2015, Loshchilov and Hutter, 2017], and it is well-known that the classical stochastic gradient descent (SGD) achieves a convergence rate of
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898aef0932f6aaecda27aba8e9903991-Paper.pdf
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Being still in its early stage, there are fundamental issues: scalability,reward design, and end-toend learning paradigm etc.
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UNION: Unsupervised 3D Object Detectionusing Object Appearance-based Pseudo-Classes
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The code is available at github.com/TedLentsch/UNION. Figure 1: UNION discovers mobile objects (e.g.
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