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AUnifiedConvergenceTheoremforStochastic OptimizationMethods
In this work, we provide a fundamental unified convergence theorem used for deriving expected and almost sure convergence results for a series of stochastic optimization methods.
Adversarial Text Generation via Feature-Mover's Distance
Liqun Chen, Shuyang Dai, Chenyang Tao, Haichao Zhang, Zhe Gan, Dinghan Shen, Yizhe Zhang, Guoyin Wang, Ruiyi Zhang, Lawrence Carin
Neural Information Processing Systems http://nips.cc/
Escaping Saddle Points in Constrained Optimization
Aryan Mokhtari, Asuman Ozdaglar, Ali Jadbabaie
Asymptotic constrained (1)has [6, 18, 21, 20]. R and closedsetC Rd introduced(1).
Probabilistic Neural Programmed Networks for Scene Generation
Zhiwei Deng, Jiacheng Chen, YIFANG FU, Greg Mori
Neural Information Processing Systems http://nips.cc/
Query K-means Clustering and the Double Dixie Cup Problem
I Chien, Chao Pan, Olgica Milenkovic
Neural Information Processing Systems http://nips.cc/