[code] [pdf] 白盒 beam search 基于梯度 字符级
Our method relies on an atomic flip operation, which swaps one token for another, based on the gradients of the one-hot input vectors. Due to efficiency of our method, we can perform adversarial training which makes the model more robust to attacks at test time. With the use of a few ...
To this end, first, we define adversarial attacks in multilabel text classification problems. We categorize attacking multilabel text classifiers as (a) positive-targeted, where the target positive label should fall out of top-k predicted labels, and (b) negative-targeted, where the target ...
@inproceedings{ebrahimi2018hotflip, title={HotFlip: White-Box Adversarial Examples for Text Classification}, author={Ebrahimi, Javid and Rao, Anyi and Lowd, Daniel and Dou, Dejing}, booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short...
bbc.php- example of text classification Regression: wineQuality.php- regression model to assess the quality of the wine License PHP-ML is released under the MIT Licence. See the bundled LICENSE file for details. Author Arkadiusz Kondas (@ArkadiuszKondas) ...
where _ is the frequency of the i-th word, calculated within all training examples. 若如此做,再加上一个噪声如下: 我们就完成了这次对抗样本对某个文本分类器的攻击。 你们可能很快就发现了问题,但是在这之前还是讨论一下这样做的另一个意义,便是这种 Adversarial Training 更类似于一种 Regularization 的...
Building a set of classifiers by iteratively applying a classification algorithm and then selecting a good classifier from the set. In this step, S-EM uses the Expectation Maximization (EM) algorithm [7] with a NB classifier, while PEBL and Roc-SVM use SVM. Both S-EM and Roc-SVM have so...
Hierarchical text classification (HTC) approaches have recently attracted a lot of interest on the part of researchers in human language technology and machine learning, since they have been shown to bring about equal, if not better, classification accuracy with respect to their "flat" counterparts ...
Summary Learning from positive and unlabeled examples (PU learning) is a partially supervised classification that is frequently used in Web and text retrieval system. The merit of PU learning is that it can get good performance with less manual work. Motivated by transfer learning, this paper ...
classification paper, the aim of the text would be, without doubt, to lay out various categories related to the theme of discussion, and probably explain why some objects are a better fit for one division or the other. Your text should be able to explain fully all points that may have ...