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Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
Adversarial training [10, 16, 18], which injects adversarially perturbed dataintotraining data,isapromising approach. Many other heuristics have been developed to make neural networks insensitive against small perturbations on inputs.
Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction
Yunji Kim, Seonghyeon Nam, In Cho, Seon Joo Kim
Neural Information Processing Systems http://nips.cc/
Unified Sample-Optimal Property Estimation in Near-Linear Time
Let k be the collection of distributions over the alphabet[k]:= {1,2,...,k}, and [k] be the set of finite sequences over[k].
A convex program for bilinear inversion of sparse vectors
Alireza Aghasi, Ali Ahmed, Paul Hand, Babhru Joshi
Neural Information Processing Systems http://nips.cc/
