馒头and花卷

摘要： Ma X, Li B, Wang Y, et al. Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality[J]. arXiv: Learning, 2018. @article{ma2018charact 阅读全文

摘要： Wang H, Wang Y, Zhou Z, et al. CosFace: Large Margin Cosine Loss for Deep Face Recognition[C]. computer vision and pattern recognition, 2018: 5265-527 阅读全文

摘要： Pang T, Du C, Zhu J, et al. Max-Mahalanobis Linear Discriminant Analysis Networks[C]. international conference on machine learning, 2018: 4013-4022. @ 阅读全文

摘要： Wang X., He K., Guo C., Weinberger K., Hopcroft H., AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets. arX 阅读全文

摘要： Xiao C, Li B, Zhu J, et al. Generating Adversarial Examples with Adversarial Networks[J]. arXiv: Cryptography and Security, 2018. @article{xiao2018gen 阅读全文

摘要： Wang Y, Zou D, Yi J, et al. Improving Adversarial Robustness Requires Revisiting Misclassified Examples[C]. international conference on learning repre 阅读全文

摘要： Kingma D P, Ba J. Adam: A Method for Stochastic Optimization[J]. arXiv: Learning, 2014. @article{kingma2014adam:, title=, author={Kingma, Diederik P a 阅读全文

摘要： Moosavidezfooli S, Fawzi A, Fawzi O, et al. Universal Adversarial Perturbations[C]. computer vision and pattern recognition, 2017: 86-94. @article{moo 阅读全文

摘要： Schmidt L, Santurkar S, Tsipras D, et al. Adversarially Robust Generalization Requires More Data[C]. neural information processing systems, 2018: 5014 阅读全文

摘要： Samangouei P, Kabkab M, Chellappa R, et al. Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models.[J]. arXiv: Comput 阅读全文

摘要： Athalye A, Carlini N, Wagner D, et al. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples[J]. arXiv: 阅读全文

摘要： [TOC] "Keskar N S, Mudigere D, Nocedal J, et al. On Large Batch Training for Deep Learning: Generalization Gap and Sharp Minima[J]. arXiv: Learning, 2 阅读全文

摘要： Richard D. Gill, Product integration 一般的积分是指黎曼积分, 其计算是把区域无限细分求和并取极限, 有另外一种积分是把区域无限细分求积并取极限, 这个在生存模型中有很多应用. 生存模型 设生存的时间为随机变量$T$, 则生存函数定义为 \[ S(t):= \ma 阅读全文

摘要： Koh P W, Liang P. Understanding black-box predictions via influence functions[C]. international conference on machine learning, 2017: 1885-1894. @arti 阅读全文

摘要： Ilyas A, Santurkar S, Tsipras D, et al. Adversarial Examples Are Not Bugs, They Are Features[C]. neural information processing systems, 2019: 125-136. 阅读全文

摘要： Moosavidezfooli S, Fawzi A, Frossard P, et al. DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks[C]. computer vision and pattern rec 阅读全文

摘要： Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, Ananthram Swami, The Limitations of Deep Learning in Adversarial Set 阅读全文

摘要： Alexey Kurakin, Ian J. Goodfellow, Samy Bengio, ADVERSARIAL EXAMPLES IN THE PHYSICAL WORLD 概 有很多种方法能够生成对抗样本(adversarial samples), 但是真实世界中是否存在这样的对抗样本呢? 阅读全文

摘要： Pirmin Lemberger, Ivan Panico, A Primer on Domain Adaptation Theory and Applications, 2019. 概 机器学习分为训练和测试俩步骤, 且往往假设训练样本的分布和测试样本的分布是一致的, 但是这种情况在实际中并不一定 阅读全文

摘要： Wang J, Chen Y, Chakraborty R, et al. Orthogonal Convolutional Neural Networks.[J]. arXiv: Computer Vision and Pattern Recognition, 2019. @article{wan 阅读全文