Shi Feng
Shi Feng
Verifierad e-postadress på uchicago.edu - Startsida
Citeras av
Citeras av
Universal adversarial triggers for attacking and analyzing NLP
E Wallace, S Feng, N Kandpal, M Gardner, S Singh
arXiv preprint arXiv:1908.07125, 2019
Calibrate before use: Improving few-shot performance of language models
Z Zhao, E Wallace, S Feng, D Klein, S Singh
International Conference on Machine Learning, 12697-12706, 2021
Pathologies of Neural Models Make Interpretations Difficult
S Feng, E Wallace, A Grissom II, M Iyyer, P Rodriguez, J Boyd-Graber
EMNLP, 2018
Trick me if you can: Human-in-the-loop generation of adversarial examples for question answering
E Wallace, P Rodriguez, S Feng, I Yamada, J Boyd-Graber
Transactions of the Association for Computational Linguistics 7, 387-401, 2019
What can ai do for me? evaluating machine learning interpretations in cooperative play
S Feng, J Boyd-Graber
Proceedings of the 24th International Conference on Intelligent User …, 2019
Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation
S Feng, S Liu, N Yang, M Li, M Zhou, KQ Zhu
COLING, 2016
Knowledge-based semantic embedding for machine translation
C Shi, S Liu, S Ren, S Feng, M Li, M Zhou, X Sun, H Wang
Proceedings of the 54th Annual Meeting of the Association for Computational …, 2016
Concealed data poisoning attacks on nlp models
E Wallace, TZ Zhao, S Feng, S Singh
arXiv preprint arXiv:2010.12563, 2020
Interpreting neural networks with nearest neighbors
E Wallace, S Feng, J Boyd-Graber
arXiv preprint arXiv:1809.02847, 2018
Understanding impacts of high-order loss approximations and features in deep learning interpretation
S Singla, E Wallace, S Feng, S Feizi
International Conference on Machine Learning, 5848-5856, 2019
Misleading failures of partial-input baselines
S Feng, E Wallace, J Boyd-Graber
arXiv preprint arXiv:1905.05778, 2019
Quizbowl: The case for incremental question answering
P Rodriguez, S Feng, M Iyyer, H He, J Boyd-Graber
arXiv preprint arXiv:1904.04792, 2019
A.; Iyyer, M.; Rodriguez, P.; and Boyd-Graber, J. 2018. Pathologies of neural models make interpretations difficult
S Feng, E Wallace, II Grissom
proceedings of the 2018 conference on empirical methods in natural language …, 0
Human-computer question answering: The case for quizbowl
J Boyd-Graber, S Feng, P Rodriguez
The NIPS'17 Competition: Building Intelligent Systems, 169-180, 2018
How pre-trained word representations capture commonsense physical comparisons
P Goel, S Feng, J Boyd-Graber
Proceedings of the First Workshop on Commonsense Inference in Natural …, 2019
Machine explanations and human understanding
C Chen, S Feng, A Sharma, C Tan
arXiv preprint arXiv:2202.04092, 2022
Active example selection for in-context learning
Y Zhang, S Feng, C Tan
arXiv preprint arXiv:2211.04486, 2022
The umd neural machine translation systems at wmt17 bandit learning task
A Sharaf, S Feng, K Nguyen, K Brantley, H Daumé III
arXiv preprint arXiv:1708.01318, 2017
Introduction to NIPS 2017 Competition Track
S Escalera, M Weimer, M Burtsev, V Malykh, V Logacheva, R Lowe, ...
The NIPS'17 Competition: Building Intelligent Systems, 1-23, 2018
Learning Human-Compatible Representations for Case-Based Decision Support
H Liu, Y Tian, C Chen, S Feng, Y Chen, C Tan
arXiv preprint arXiv:2303.04809, 2023
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Artiklar 1–20