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Robustness

531 papers

Papers per year

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6
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Papers

NAT: Noise-Aware Training for Robust Neural Sequence Labeling ACL 2020
On the Robustness of Language Encoders against Grammatical Errors ACL 2020
Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation ACL 2020
End-to-End Bias Mitigation by Modelling Biases in Corpora ACL 2020
Towards Robustifying NLI Models Against Lexical Dataset Biases ACL 2020
What’s in a Name? Are BERT Named Entity Representations just as Good for any other Name? ACL 2020
Single-Step Adversarial Training With Dropout Scheduling CVPR 2020
Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations CVPR 2020
Modeling Biological Immunity to Adversarial Examples CVPR 2020
Compositional Convolutional Neural Networks: A Deep Architecture With Innate Robustness to Partial Occlusion CVPR 2020
GeoDA: A Geometric Framework for Black-Box Adversarial Attacks CVPR 2020
Clean-Label Backdoor Attacks on Video Recognition Models CVPR 2020
Self-Robust 3D Point Recognition via Gather-Vector Guidance CVPR 2020
More Bang for Your Buck: Natural Perturbation for Robust Question Answering EMNLP 2020
Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data EMNLP 2020
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation EMNLP 2020
Towards Debiasing NLU Models from Unknown Biases EMNLP 2020
Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness EMNLP 2020
Enhance Robustness of Sequence Labelling with Masked Adversarial Training EMNLP 2020
Improving QA Generalization by Concurrent Modeling of Multiple Biases EMNLP 2020
Evaluating Models’ Local Decision Boundaries via Contrast Sets EMNLP 2020
Do Models of Mental Health Based on Social Media Data Generalize? EMNLP 2020
Reevaluating Adversarial Examples in Natural Language EMNLP 2020
Second-Order NLP Adversarial Examples EMNLP 2020
Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data EMNLP 2020