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Ng I., Fang Z., Zhu S., Chen Z. and Wang J. Masked Gradient-Based Causal Structure Learning. arXiv preprint arXiv:1911.10500, 2019. 概 非线性, 自动地学习因果图. 主 阅读全文
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motivation BBN 对于处理长尾问题非常有效, 我在想, 能不能类似地用在鲁棒问题上. 思想很简单, 就是上面用干净数据, 下面用对抗样本(其用$\alpha=0.5$的eval mode 生成), 但是结果非常差. settings - batch_size: 128 - beta1: 阅读全文
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DAGs with NO TEARS: Continuous Optimization for Structure Learning Zheng X., Aragam B., Ravikumar P. and Xing E. DAGs with NO TEARS: Continuous Optimi 阅读全文
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Jang E., Gu S. and Poole B. Categorical reparameterization with gumbel-softmax. In International Conference On Learning Representations (ICLR), 2017. 阅读全文
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概 感觉这个分布的含义很有用啊, 能预测‘最大', 也就是自然灾害, 太牛了. 主要内容 定义 [Gumbel distribution-wiki](Gumbel distribution - Wikipedia) 其分布函数和概率密度函数分别为: \[ F(x; \mu, \beta) = e^{ 阅读全文
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Locatello F., Bauer S., Lucic M., R"{a}tsch G., Gelly S. Sch"{o}lkopf and Bachem Olivier. Challenging common assumptions in the unsupervised learning 阅读全文
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源 Exponential moving average (EMA) 是一个非常有用的trick, 起到加速训练的作用. 近来发现, 该技巧还可以用于提高网络鲁棒性(约1% ~ 2%). EMA的流程很简单, $f(\cdot;\theta)$是我们用于训练的网络, 则在每次迭代结束后进行: \[ 阅读全文
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Ioffe S. and Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In International Conference on M 阅读全文
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Rosenbaum P. and Rubin D. The Central Role of the Propensity Score in Observational Studies For Causal Effects. Biometrika, 1983, 70(1): 41-55. Propen 阅读全文
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Sehwag V., Mahloujifar S., Handina T., Dai S., Xiang C., Chiang M. and Mittal P. Improving adversarial robustness using proxy Distributions. arXiv pre 阅读全文
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> Prabhushankar M., Kwon G., Temel D. and AlRegib G. Contrastive explanation in neural networks. In 2020 IEEE International Conference on Image Proces 阅读全文
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Gowal S., Dvijotham K., Stanforth R., Bunel R., Qin C., Uesato J., Arandjelovic R., Mann T. & Kohli P. Scalable verified training for provably robust 阅读全文
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Zhang H., Zhang Z., Odena A. and Lee H. CONSISTENCY REGULARIZATION FOR GENERATIVE ADVERSARIAL NETWORKS. ICLR, 2020. Zhao Z., Singh S., Lee H., Zhang Z 阅读全文
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Zhao S., Liu Z., Lin J., Zhu J. and Han S. Differentiable Augmentation for Data-Efficient GAN Training. NIPS, 2020. Karras T., Aittala M., Hellsten J. 阅读全文
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Zhou B., Khosla A., Lapedriza A., Oliva A. and Torralba A. Learning Deep Features for Discriminative Localization. CVPR, 2016. Selvaraju R., Das A., V 阅读全文
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Niu Y., Tang K., Zhang H., Lu Z., Hua X. and Wen J. Counterfactual VQA: A Cause-Effect Look at Language Bias. CVPR, 2021. 概 利用因果分析消除VQA(Visual Questio 阅读全文
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Zhang D., Zhang H., Tang J., Hua X. and Sun Q. Causal Intervention for Weakly-Supervised Semantic Segmentation. NIPS, 2020. 概 这篇文章从因果关系的角度剖析如何提升弱监督语义分 阅读全文
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Judea Pearl. Direct and indirect effects. In Proceedings of the 17th conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers 阅读全文
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Hern$'{a}$n M. and Robins J. Causal Inference: What If. Neal B. Introduction to Causal Inference. graph LR A(A) --> Y(Y) graph LR L(L) -->A(A) --> Y(Y 阅读全文
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Hern$'$n M. and Robins J. Causal Inference: What If. 初次提到the target trial在 page 37. 本章提到的direct causal effect感觉还是挺重要的, 就是感觉讲得太少了. 22.1 The target tria 阅读全文