| Learning compact recurrent neural networks Z Lu, V Sindhwani, TN Sainath Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International …, 2016 | 109 | 2016 |
| Speech sentiment analysis via pre-trained features from end-to-end asr models Z Lu, L Cao, Y Zhang, CC Chiu, J Fan ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 81 | 2020 |
| How to scale up kernel methods to be as good as deep neural nets Z Lu, A May, K Liu, AB Garakani, D Guo, A Bellet, L Fan, M Collins, ... arXiv preprint arXiv:1411.4000, 2014 | 75* | 2014 |
| Kernel approximation methods for speech recognition A May, AB Garakani, Z Lu, D Guo, K Liu, A Bellet, L Fan, M Collins, D Hsu, ... The Journal of Machine Learning Research 20 (1), 2121-2156, 2019 | 34 | 2019 |
| A large scale speech sentiment corpus E Chen, Z Lu, H Xu, L Cao, Y Zhang, J Fan Proceedings of the Twelfth Language Resources and Evaluation Conference …, 2020 | 28 | 2020 |
| Improving streaming automatic speech recognition with non-streaming model distillation on unsupervised data T Doutre, W Han, M Ma, Z Lu, CC Chiu, R Pang, A Narayanan, A Misra, ... ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 26 | 2021 |
| Exploring targeted universal adversarial perturbations to end-to-end asr models Z Lu, W Han, Y Zhang, L Cao arXiv preprint arXiv:2104.02757, 2021 | 19 | 2021 |
| Selecting β-Divergence for Nonnegative Matrix Factorization by Score Matching Z Lu, Z Yang, E Oja Artificial Neural Networks and Machine Learning–ICANN 2012: 22nd …, 2012 | 19 | 2012 |
| E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASR W Ronny Huang, S Chang, D Rybach, R Prabhavalkar, TN Sainath, ... arXiv e-prints, arXiv: 2204.10749, 2022 | 18* | 2022 |
| Hyper-parameter tuning under a budget constraint Z Lu, CK Chiang, F Sha arXiv preprint arXiv:1902.00532, 2019 | 16 | 2019 |
| Less is more: Removing text-regions improves clip training efficiency and robustness L Cao, B Zhang, C Chen, Y Yang, X Du, W Zhang, Z Lu, Y Zheng arXiv preprint arXiv:2305.05095, 2023 | 15 | 2023 |
| Improving the fusion of acoustic and text representations in RNN-T C Zhang, B Li, Z Lu, TN Sainath, S Chang ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 15 | 2022 |
| A comparison between deep neural nets and kernel acoustic models for speech recognition Z Lu, D Quo, AB Garakani, K Liu, A May, A Bellet, L Fan, M Collins, ... 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 14 | 2016 |
| Uncertainty estimation with infinitesimal jackknife, its distribution and mean-field approximation Z Lu, E Ie, F Sha arXiv preprint arXiv:2006.07584, 2020 | 12 | 2020 |
| Unsupervised data selection via discrete speech representation for asr Z Lu, Y Wang, Y Zhang, W Han, Z Chen, P Haghani arXiv preprint arXiv:2204.01981, 2022 | 10 | 2022 |
| Input length matters: Improving RNN-T and MWER training for long-form telephony speech recognition Z Lu, Y Pan, T Doutre, P Haghani, L Cao, R Prabhavalkar, C Zhang, ... arXiv preprint arXiv:2110.03841, 2021 | 10 | 2021 |
| Mean-field approximation to Gaussian-softmax integral with application to uncertainty estimation Z Lu, E Ie, F Sha arXiv preprint arXiv:2006.07584, 2020 | 7 | 2020 |
| Apple intelligence foundation language models T Gunter, Z Wang, C Wang, R Pang, A Narayanan, A Zhang, B Zhang, ... arXiv preprint arXiv:2407.21075, 2024 | 5 | 2024 |
| Direct large language model alignment through self-rewarding contrastive prompt distillation A Liu, H Bai, Z Lu, X Kong, S Wang, J Shan, M Cao, L Wen arXiv preprint arXiv:2402.11907, 2024 | 5 | 2024 |
| Input length matters: An empirical study of RNN-T and MWER training for long-form telephony speech recognition Z Lu, Y Pan, T Doutre, L Cao, R Prabhavalkar, C Zhang, T Strohman arXiv preprint arXiv:2110.03841, 2021 | 5 | 2021 |
Liangliang CaoApple IncE-mailová adresa ověřena na: apple.com
