Wu, Yonghui

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al. (2016) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 5,319

Conformer: Convolution-augmented Transformer for Speech Recognition

Gulati, A., Qin, J., Chiu, C.-C., Parmar, N., Zhang, Y., Yu, J., Han, W., Wang, S., Zhang, Z., Wu, Y., et al. (2020) · Interspeech 2020

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 3,150

Conformer: Convolution-augmented Transformer for Speech Recognition

Gulati, A., Qin, J., Chiu, C.-C., Parmar, N., Zhang, Y., Yu, J., Han, W., Wang, S., Zhang, Z., Wu, Y., et al. (2020) · arXiv

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 3,150

Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions

Shen, J., Pang, R., Weiss, R. J., Schuster, M., Jaitly, N., Yang, Z., Chen, Z., Zhang, Y., Wang, Y., Skerrv-Ryan, R., et al. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 2,791

Tacotron: towards end-to-end speech synthesis

Wang, Y., Skerry-Ryan, R. J., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., Yang, Z., Xiao, Y., Chen, Z., Bengio, S., et al. (2017) · Interspeech 2017

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 1,746

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

Huang, Y., Cheng, Y., Bapna, A., Firat, O., Chen, M. X., Chen, D., Lee, H., Ngiam, J., Le, Q. V., Wu, Y., et al. (2018) · arXiv

Parallel Computing and Optimization TechniquesAdvanced Neural Network Applications

Cited by 1,180

State-of-the-Art Speech Recognition with Sequence-to-Sequence Models

Chiu, C.-C., Sainath, T. N., Wu, Y., Prabhavalkar, R., Nguyen, P., Chen, Z., Kannan, A., Weiss, R. J., Rao, K., Gonina, E., et al. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 1,043

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

A large language model for electronic health records

Yang, X., Chen, A., PourNejatian, N., Shin, H. C., Smith, K. E., Parisien, C., Compas, C., Martin, C., Costa, A. B., Flores, M. G., et al. (2022) · npj Digital Medicine

Machine Learning in HealthcareTopic Modeling

Cited by 847

Streaming End-to-end Speech Recognition for Mobile Devices

He, Y., Sainath, T. N., Prabhavalkar, R., McGraw, I., Alvarez, R., Zhao, D., Rybach, D., Kannan, A., Wu, Y., Pang, R., et al. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 601

Transfer learning from speaker verification to multispeaker text-to-speech synthesis

Jia, Y., Zhang, Y., Weiss, R. J., Wang, Q., Shen, J., Ren, F., Chen, Z., Nguyen, P., Pang, R., Moreno, I. L., et al. (2018) · arXiv

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 519

A study of generative large language model for medical research and healthcare

Peng, C., Yang, X., Chen, A., Smith, K. E., PourNejatian, N., Costa, A. B., Martin, C., Flores, M. G., Zhang, Y., Magoc, T., et al. (2023) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationTopic Modeling

Cited by 408

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

w2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training

Chung, Y.-A., Zhang, Y., Han, W., Chiu, C.-C., Qin, J., Pang, R., & Wu, Y. (2021) · 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 293

Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges

Arivazhagan, N., Bapna, A., Firat, O., Lepikhin, D., Johnson, M., Krikun, M., Chen, M. X., Cao, Y., Foster, G., Cherry, C., et al. (2019) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 279

An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model

Kannan, A., Wu, Y., Nguyen, P., Sainath, T. N., Chen, Z., & Prabhavalkar, R. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 253

Extracting social determinants of health from electronic health records using natural language processing: a systematic review

Patra, B. G., Sharma, M. M., Vekaria, V., Adekkanattu, P., Patterson, O. V., Glicksberg, B., Lepow, L. A., Ryu, E., Biernacka, J. M., Furmanchuk, A., et al. (2021) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareBiomedical Text Mining and Ontologies

Cited by 248

Gmail Smart Compose: Real-Time Assisted Writing

Chen, M. X., Lee, B. N., Bansal, G., Cao, Y., Zhang, S., Lu, J., Tsay, J., Wang, Y., Dai, A. M., Chen, Z., et al. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Natural Language Processing TechniquesTopic Modeling

Cited by 169

BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition

Zhang, Y., Park, D. S., Han, W., Qin, J., Gulati, A., Shor, J., Jansen, A., Xu, Y., Huang, Y., Wang, S., et al. (2022) · IEEE Journal of Selected Topics in Signal Processing

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 161

A hybrid system for temporal information extraction from clinical text

Tang, B., Wu, Y., Jiang, M., Chen, Y., Denny, J. C., & Xu, H. (2013) · Journal of the American Medical Informatics Association

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 117

Early prediction of Alzheimer's disease and related dementias using real-world electronic health records

Li, Q., Yang, X., Xu, J., Guo, Y., He, X., Hu, H., Lyu, T., Marra, D., Miller, A., Smith, G., et al. (2023) · Alzheimer's & Dementia

Machine Learning in HealthcareDementia and Cognitive Impairment Research

Cited by 113

International Classification of Diseases, Tenth Revision, Clinical Modification social determinants of health codes are poorly used in electronic health records

Guo, Y., Chen, Z., Xu, K., George, T. J., Wu, Y., Hogan, W., Shenkman, E. A., & Bian, J. (2020) · Medicine

Medical Coding and Health InformationElectronic Health Records Systems

Cited by 95

Identification of important factors in an inpatient fall risk prediction model to improve the quality of care using EHR and electronic administrative data: A machine-learning approach

Lindberg, D. S., Prosperi, M., Bjarnadottir, R. I., Thomas, J., Crane, M., Chen, Z., Shear, K., Solberg, L. M., Snigurska, U. A., Wu, Y., et al. (2020) · International Journal of Medical Informatics

Balance, Gait, and Falls PreventionMachine Learning in Healthcare

Cited by 95

Assessing the practice of data quality evaluation in a national clinical data research network through a systematic scoping review in the era of real-world data

Bian, J., Lyu, T., Loiacono, A., Viramontes, T. M., Lipori, G., Guo, Y., Wu, Y., Prosperi, M., George, T. J., Harle, C. A., et al. (2020) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsData Quality and Management

Cited by 92