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- W2963696733 abstract "Deep neural networks have achieved remarkable results across many language processing tasks, however these methods are highly sensitive to noise and adversarial attacks. We present a regularization based method for limiting network sensitivity to its inputs, inspired by ideas from computer vision, thus learning models that are more robust. Empirical evaluation over a range of sentiment datasets with a convolutional neural network shows that, compared to a baseline model and the dropout method, our method achieves superior performance over noisy inputs and out-of-domain data." @default.
- W2963696733 created "2019-07-30" @default.
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- W2963696733 date "2016-01-01" @default.
- W2963696733 modified "2023-09-25" @default.
- W2963696733 title "Learning Robust Representations of Text" @default.
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- W2963696733 doi "https://doi.org/10.18653/v1/d16-1207" @default.
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