| BERTScore: Evaluating Text Generation with BERT T Zhang, V Kishore, F Wu, KQ Weinberger, Y Artzi Eighth International Conference on Learning Representations, 2020 | 4764 | 2020 |
| Simplifying Graph Convolutional Networks F Wu, T Zhang, AH Souza Jr, C Fifty, T Yu, KQ Weinberger Proceedings of the 36th International Conference on Machine Learning, 6861--6871, 2019 | 3482 | 2019 |
| On fairness and calibration G Pleiss, M Raghavan, F Wu, J Kleinberg, KQ Weinberger Advances in neural information processing systems 30, 2017 | 1030 | 2017 |
| Multi-scale dense convolutional networks for efficient prediction G Huang, D Chen, T Li, F Wu, L Van Der Maaten, KQ Weinberger Sixth International Conference on Learning Representations, 2018 | 1022* | 2018 |
| Pay Less Attention with Lightweight and Dynamic Convolutions F Wu, A Fan, A Baevski, YN Dauphin, M Auli Seventh International Conference on Learning Representations, 2019 | 676 | 2019 |
| Revisiting few-sample BERT fine-tuning T Zhang, F Wu, A Katiyar, KQ Weinberger, Y Artzi arXiv preprint arXiv:2006.05987, 2020 | 430 | 2020 |
| An empirical study on evaluation metrics of generative adversarial networks Q Xu, G Huang, Y Yuan, C Guo, Y Sun, F Wu, K Weinberger arXiv preprint arXiv:1806.07755, 2018 | 350 | 2018 |
| AH d. Souza Jr, C. Fifty, T. Yu, and KQ Weinberger,“Simplifying graph convolutional networks,” F Wu, T Zhang arXiv preprint arXiv:1902.07153 3, 2019 | 214 | 2019 |
| On feature normalization and data augmentation B Li, F Wu, SN Lim, S Belongie, KQ Weinberger Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 169 | 2021 |
| Positional normalization B Li, F Wu, KQ Weinberger, S Belongie Advances in Neural Information Processing Systems 32, 2019 | 109 | 2019 |
| E-branchformer: Branchformer with enhanced merging for speech recognition K Kim, F Wu, Y Peng, J Pan, P Sridhar, KJ Han, S Watanabe 2022 IEEE Spoken Language Technology Workshop (SLT), 84-91, 2023 | 82 | 2023 |
| Attention-based quantum tomography P Cha, P Ginsparg, F Wu, J Carrasquilla, PL McMahon, EA Kim Machine Learning: Science and Technology 3 (1), 01LT01, 2021 | 75 | 2021 |
| Slue: New benchmark tasks for spoken language understanding evaluation on natural speech S Shon, A Pasad, F Wu, P Brusco, Y Artzi, K Livescu, KJ Han ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 73 | 2022 |
| Effective string processing and matching for author disambiguation WS Chin, YC Juan, Y Zhuang, F Wu, HY Tung, T Yu, JP Wang, CX Chang, ... Proceedings of the 2013 KDD Cup 2013 workshop, 1-9, 2013 | 44 | 2013 |
| Performance-efficiency trade-offs in unsupervised pre-training for speech recognition F Wu, K Kim, J Pan, KJ Han, KQ Weinberger, Y Artzi ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 40 | 2022 |
| Wav2seq: Pre-training speech-to-text encoder-decoder models using pseudo languages F Wu, K Kim, S Watanabe, KJ Han, R McDonald, KQ Weinberger, Y Artzi ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 34 | 2023 |
| Combining global models for parsing universal dependencies T Shi, FG Wu, X Chen, Y Cheng Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw …, 2017 | 31 | 2017 |
| Making paper reviewing robust to bid manipulation attacks R Wu, C Guo, F Wu, R Kidambi, L Van Der Maaten, K Weinberger International Conference on Machine Learning, 11240-11250, 2021 | 29 | 2021 |
| Structured pruning of self-supervised pre-trained models for speech recognition and understanding Y Peng, K Kim, F Wu, P Sridhar, S Watanabe ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 28 | 2023 |
| SLUE phase-2: A benchmark suite of diverse spoken language understanding tasks S Shon, S Arora, CJ Lin, A Pasad, F Wu, R Sharma, WL Wu, HY Lee, ... arXiv preprint arXiv:2212.10525, 2022 | 26 | 2022 |
Kilian WeinbergerCornell UniversityBestätigte E-Mail-Adresse bei cornell.edu
