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Large neural models have demonstrated human-level performance on language and vision benchmarks, while their performance degrades considerably on adversarial or out-of-distribution samples.
Invariant risk minimization, 2019
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A* sampling
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Enhanced LSTM for natural language inference
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Exact sampling with integer linear programs and random perturbations
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The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2016
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SQuAD: 100, 000+ questions for machine comprehension of text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
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Balog, M., Tripuraneni, N., Ghahramani, Z., and Weller, A · 2017
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Zellers, R., Bisk, Y., Schwartz, R., and Choi, Y · 2018
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Alcorn, M. A., Li, Q., Gong, Z., Wang, C., Mai, L., Ku, W.-S., and Nguyen, A · 2019
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Don’t take the premise for granted: Mitigating artifacts in natural language inference
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Adversarial examples for evaluating reading comprehension systems
Jia, R. and Liang, P · 2017
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Social bias in elicited natural language inferences
Rudinger, R., May, C., and Durme, B. V · 2017
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Evaluating compositionality in sentence embeddings
Dasgupta, I., Guo, D., Stuhlmüller, A., Gershman, S. J., and Goodman, N. D · 2018
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Adversarial removal of demographic attributes from text data
Elazar, Y. and Goldberg, Y · 2018
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Robust physical-world attacks on deep learning models
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., and Song, D. X · 2018
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From lifestyle vlogs to everyday interactions
Fouhey, D. F., Kuo, W.-c., Efros, A. A., and Malik, J · 2018
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Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Clark, C., Yatskar, M., and Zettlemoyer, L · 2019
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Randaugment: Practical data augmentation with no separate search
Cubuk, E. D., Zoph, B., Shlens, J., and Le, Q. V · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Are we modeling the task or the annotator? An investigation of annotator bias in natural language understanding datasets
Geva, M., Goldberg, Y., and Berant, J · 2019
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Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., and Song, D · 2019
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Stochastic beams and where to find them: The gumbel-top-k trick for sampling sequences without replacement
Kool, W., van Hoof, H., and Welling, M · 2019
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REPAIR: Removing representation bias by dataset resampling
Li, Y. C. and Vasconcelos, N · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q · 2019
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Huggingface’s transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., and Brew, J · 2019
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HellaSwag: Can a machine really finish your sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y · 2019
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An investigation of why overparameterization exacerbates spurious correlations
Sagawa, S., Raghunathan, A., Koh, P. W., and Liang, P · 2020
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Hypothesis only baselines in natural language inference
Poliak, A., Naradowsky, J., Haldar, A., Rudinger, R., and Van Durme, B · 2023
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