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Found 8 papers, 2 papers with code
Adversarial Example Games
We introduce Adversarial Example Games (AEG), a framework that models the crafting of adversarial examples as a min-max game between a generator of attacks and a classifier.
Generalizable Adversarial Attacks with Latent Variable Perturbation Modelling
Adversarial attacks on deep neural networks traditionally rely on a constrained optimization paradigm, where an optimization procedure is used to obtain a single adversarial perturbation for a given input example.
Clustering-Oriented Representation Learning with Attractive-Repulsive Loss
The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the training data; building useful representations is not a necessary byproduct of this objective.
Let's do it ``again'': A First Computational Approach to Detecting Adverbial Presupposition Triggers
We introduce the novel task of predicting adverbial presupposition triggers, which is useful for natural language generation tasks such as summarization and dialogue systems.
Let's do it "again": A First Computational Approach to Detecting Adverbial Presupposition Triggers
We introduce the task of predicting adverbial presupposition triggers such as also and again.
The CLaC Discourse Parser at CoNLL-2016
This paper describes our submission "CLaC" to the CoNLL-2016 shared task on shallow discourse parsing.
N-gram and Neural Language Models for Discriminating Similar Languages
This paper describes our submission (named clac) to the 2016 Discriminating Similar Languages (DSL) shared task.
