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Robustness

133 papers

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Papers

Sorting through the noise: Testing robustness of information processing in pre-trained language models EMNLP 2021
Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation EMNLP 2021
Sometimes We Want Ungrammatical Translations EMNLP 2021
On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world Noise EMNLP 2021
Understanding Model Robustness to User-generated Noisy Texts EMNLP 2021
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks NIPS 2020
Improving robustness against common corruptions by covariate shift adaptation NIPS 2020
On the Trade-off between Adversarial and Backdoor Robustness NIPS 2020
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness NIPS 2020
Lipschitz-Certifiable Training with a Tight Outer Bound NIPS 2020
Fairness for Robust Log Loss Classification AAAI 2020
Towards Certificated Model Robustness Against Weight Perturbations AAAI 2020
Web-Supervised Network with Softly Update-Drop Training for Fine-Grained Visual Classification AAAI 2020
Leakage-Robust Classifier via Mask-Enhanced Training (Student Abstract) AAAI 2020
Training Noise-Robust Deep Neural Networks via Meta-Learning CVPR 2020
Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations CVPR 2020
When NAS Meets Robustness: In Search of Robust Architectures Against Adversarial Attacks CVPR 2020
Benchmarking the Robustness of Semantic Segmentation Models CVPR 2020
Fine-Tuning MT systems for Robustness to Second-Language Speaker Variations EMNLP 2020
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness NIPS 2019
Self-Critical Reasoning for Robust Visual Question Answering NIPS 2019
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers NIPS 2019
On Single Source Robustness in Deep Fusion Models NIPS 2019
Interpretation of Neural Networks Is Fragile AAAI 2019
A Novel Framework for Robustness Analysis of Visual QA Models AAAI 2019