Language-independent compound splitting with morphological operations. - Klaus Macherey; Andrew M. Dai; David Talbot; Ashok C. Popat; Franz Josef Och (ACL, 2011)

The Grouped Author-Topic Model for Unsupervised Entity Resolution. - Andrew M. Dai; Amos J. Storkey (ICANN (1), 2011)

Semi-supervised Sequence Learning. - Andrew M. Dai; Quoc V. Le (NIPS, 2015)

Document Embedding with Paragraph Vectors. - Andrew M. Dai; Christopher Olah; Quoc V. Le (CoRR, 2015)

The Supervised Hierarchical Dirichlet Process. - Andrew M. Dai; Amos J. Storkey (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Generating Sentences from a Continuous Space. - Samuel R. Bowman; Luke Vilnis; Oriol Vinyals; Andrew M. Dai; Rafal Józefowicz; Samy Bengio (CoNLL, 2016)

Virtual Adversarial Training for Semi-Supervised Text Classification. - Takeru Miyato; Andrew M. Dai; Ian J. Goodfellow (CoRR, 2016)

HyperNetworks. - David Ha; Andrew M. Dai; Quoc V. Le (ICLR (Poster), 2017)

Adversarial Training Methods for Semi-Supervised Text Classification. - Takeru Miyato; Andrew M. Dai; Ian J. Goodfellow (ICLR (Poster), 2017)

Who Said What: Modeling Individual Labelers Improves Classification. - Melody Y. Guan; Varun Gulshan; Andrew M. Dai; Geoffrey E. Hinton (AAAI, 2018)

AirDialogue: An Environment for Goal-Oriented Dialogue Research. - Wei Wei; Quoc V. Le; Andrew M. Dai; Jia Li (EMNLP, 2018)

MaskGAN: Better Text Generation via Filling in the _______. - William Fedus; Ian J. Goodfellow; Andrew M. Dai (ICLR (Poster), 2018)

Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step. - William Fedus; Mihaela Rosca; Balaji Lakshminarayanan; Andrew M. Dai; Shakir Mohamed; Ian J. Goodfellow (ICLR (Poster), 2018)

Learning Longer-term Dependencies in RNNs with Auxiliary Losses. - Trieu H. Trinh; Andrew M. Dai; Minh-Thang Luong; Quoc V. Le (ICLR (Workshop), 2018)

Learning Longer-term Dependencies in RNNs with Auxiliary Losses. - Trieu H. Trinh; Andrew M. Dai; Thang Luong; Quoc V. Le (ICML, 2018)

Embedding Text in Hyperbolic Spaces. - Bhuwan Dhingra; Christopher J. Shallue; Mohammad Norouzi 002; Andrew M. Dai; George E. Dahl (TextGraphs@NAACL-HLT, 2018)

Scalable and accurate deep learning for electronic health records. - Alvin Rajkomar; Eyal Oren; Kai Chen 010; Andrew M. Dai; Nissan Hajaj; Peter J. Liu; Xiaobing Liu; Mimi Sun; Patrik Sundberg; Hector Yee; Kun Zhang 043; Gavin E. Duggan; et al. (CoRR, 2018)

An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation. - Cheng-Zhi Anna Huang; Ashish Vaswani; Jakob Uszkoreit; Noam Shazeer; Curtis Hawthorne; Andrew M. Dai; Matthew D. Hoffman; Douglas Eck (CoRR, 2018)

Reply: metrics to assess machine learning models. - Alvin Rajkomar; Andrew M. Dai; Mimi Sun; Michaela Hardt; Kai Chen 010; Kathryn Rough; Jeffrey Dean (npj Digit. Medicine, 2018)

Scalable and accurate deep learning with electronic health records. - Alvin Rajkomar; Eyal Oren; Kai Chen 010; Andrew M. Dai; Nissan Hajaj; Michaela Hardt; Peter J. Liu; Xiaobing Liu; Jake Marcus; Mimi Sun; Patrik Sundberg; Hector Yee; et al. (npj Digit. Medicine, 2018)

Music Transformer: Generating Music with Long-Term Structure. - Cheng-Zhi Anna Huang; Ashish Vaswani; Jakob Uszkoreit; Ian Simon; Curtis Hawthorne; Noam Shazeer; Andrew M. Dai; Matthew D. Hoffman; Monica Dinculescu; Douglas Eck (ICLR (Poster), 2019)

Gmail Smart Compose: Real-Time Assisted Writing. - Mia Xu Chen; Benjamin N. Lee; Gagan Bansal; Yuan Cao 007; Shuyuan Zhang 002; Justin Lu; Jackie Tsay; Yinan Wang; Andrew M. Dai; Zhifeng Chen; Timothy Sohn; Yonghui Wu (KDD, 2019)

Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records. - Edward Choi; Zhen Xu 006; Yujia Li; Michael W. Dusenberry; Gerardo Flores; Yuan Xue; Andrew M. Dai (CoRR, 2019)

Improved Patient Classification with Language Model Pretraining Over Clinical Notes. - Jonas Kemp; Alvin Rajkomar; Andrew M. Dai (CoRR, 2019)

Learning an Adaptive Learning Rate Schedule. - Zhen Xu 006; Andrew M. Dai; Jonas Kemp; Luke Metz (CoRR, 2019)

Federated and Differentially Private Learning for Electronic Health Records. - Stephen R. Pfohl; Andrew M. Dai; Katherine A. Heller (CoRR, 2019)

Modelling EHR timeseries by restricting feature interaction. - Kun Zhang 043; Yuan Xue; Gerardo Flores; Alvin Rajkomar; Claire Cui; Andrew M. Dai (CoRR, 2019)

Deep Physiological State Space Model for Clinical Forecasting. - Yuan Xue; Denny Zhou; Nan Du 002; Andrew M. Dai; Zhen Xu 006; Kun Zhang 043; Claire Cui (CoRR, 2019)

Natural Questions: a Benchmark for Question Answering Research. - Tom Kwiatkowski; Jennimaria Palomaki; Olivia Redfield; Michael Collins 001; Ankur P. Parikh; Chris Alberti; Danielle Epstein; Illia Polosukhin; Jacob Devlin; Kenton Lee; Kristina Toutanova; Llion Jones; et al. (Trans. Assoc. Comput. Linguistics, 2019)

Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer. - Edward Choi; Zhen Xu 006; Yujia Li; Michael Dusenberry; Gerardo Flores; Emily Xue; Andrew M. Dai (AAAI, 2020)

Capacity, Bandwidth, and Compositionality in Emergent Language Learning. - Cinjon Resnick; Abhinav Gupta 002; Jakob N. Foerster; Andrew M. Dai; Kyunghyun Cho (AAMAS, 2020)

Analyzing the role of model uncertainty for electronic health records. - Michael W. Dusenberry; Dustin Tran; Edward Choi; Jonas Kemp; Jeremy Nixon; Ghassen Jerfel; Katherine A. Heller; Andrew M. Dai (CHIL, 2020)

Explaining an increase in predicted risk for clinical alerts. - Michaela Hardt; Alvin Rajkomar; Gerardo Flores; Andrew M. Dai; Michael D. Howell; Greg Corrado; Claire Cui; Moritz Hardt (CHIL, 2020)

Flow Contrastive Estimation of Energy-Based Models. - Ruiqi Gao; Erik Nijkamp; Diederik P. Kingma; Zhen Xu 006; Andrew M. Dai; Ying Nian Wu (CVPR, 2020)

Deep State-Space Generative Model For Correlated Time-to-Event Predictions. - Yuan Xue; Denny Zhou; Nan Du 002; Andrew M. Dai; Zhen Xu 006; Kun Zhang 043; Claire Cui (KDD, 2020)

Learning to Select Best Forecast Tasks for Clinical Outcome Prediction. - Yuan Xue; Nan Du 002; Anne Mottram; Martin Seneviratne; Andrew M. Dai (NeurIPS, 2020)

Compositionality and Capacity in Emergent Languages. - Abhinav Gupta 002; Cinjon Resnick; Jakob N. Foerster; Andrew M. Dai; Kyunghyun Cho (RepL4NLP@ACL, 2020)

Learning Unstable Dynamical Systems with Time-Weighted Logarithmic Loss. - Kamil Nar; Yuan Xue; Andrew M. Dai (CoRR, 2020)

Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0). - Shailesh Bavadekar; Andrew M. Dai; John Davis; Damien Desfontaines; Ilya Eckstein; Katie Everett; Alex Fabrikant; Gerardo Flores; Evgeniy Gabrilovich; Krishna Gadepalli; Shane Glass; Rayman Huang; et al. (CoRR, 2020)

Learnability and Complexity of Quantum Samples. - Murphy Yuezhen Niu; Andrew M. Dai; Li Li; Augustus Odena; Zhengli Zhao; Vadim Smelyanskyi; Hartmut Neven; Sergio Boixo (CoRR, 2020)

MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records. - Zhen Xu 006; David R. So; Andrew M. Dai (AAAI, 2021)

Training independent subnetworks for robust prediction. - Marton Havasi; Rodolphe Jenatton; Stanislav Fort; Jeremiah Zhe Liu; Jasper Snoek; Balaji Lakshminarayanan; Andrew Mingbo Dai; Dustin Tran (ICLR, 2021)

BEDS-Bench: Behavior of EHR-models under Distributional Shift-A Benchmark. - Anand Avati; Martin Seneviratne; Emily Xue; Zhen Xu 006; Balaji Lakshminarayanan; Andrew M. Dai (CoRR, 2021)

Co-training Transformer with Videos and Images Improves Action Recognition. - Bowen Zhang 002; Jiahui Yu; Christopher Fifty; Wei Han; Andrew M. Dai; Ruoming Pang; Fei Sha (CoRR, 2021)

Finetuned Language Models are Zero-Shot Learners. - Jason Wei; Maarten Bosma; Vincent Y. Zhao; Kelvin Guu; Adams Wei Yu; Brian Lester; Nan Du 002; Andrew M. Dai; Quoc V. Le (ICLR, 2022)

GLaM: Efficient Scaling of Language Models with Mixture-of-Experts. - Nan Du 002; Yanping Huang; Andrew M. Dai; Simon Tong; Dmitry Lepikhin; Yuanzhong Xu; Maxim Krikun; Yanqi Zhou; Adams Wei Yu; Orhan Firat; Barret Zoph; Liam Fedus; et al. (ICML, 2022)

Mixture-of-Experts with Expert Choice Routing. - Yanqi Zhou; Tao Lei; Hanxiao Liu; Nan Du 002; Yanping Huang; Vincent Y. Zhao; Andrew M. Dai; Zhifeng Chen; Quoc V. Le; James Laudon (NeurIPS, 2022)

Scaling Instruction-Finetuned Language Models. - Hyung Won Chung; Le Hou; Shayne Longpre; Barret Zoph; Yi Tay; William Fedus; Eric Li; Xuezhi Wang 002; Mostafa Dehghani 001; Siddhartha Brahma; Albert Webson; Shixiang Shane Gu; et al. (CoRR, 2022)

Massively Multilingual Shallow Fusion with Large Language Models. - Ke Hu; Tara N. Sainath; Bo Li 028; Nan Du 002; Yanping Huang; Andrew M. Dai; Yu Zhang 033; Rodrigo Cabrera; Zhifeng Chen; Trevor Strohman (ICASSP, 2023)

Mind's Eye: Grounded Language Model Reasoning through Simulation. - Ruibo Liu; Jason Wei; Shixiang Shane Gu; Te-Yen Wu; Soroush Vosoughi; Claire Cui; Denny Zhou; Andrew M. Dai (ICLR, 2023)

Brainformers: Trading Simplicity for Efficiency. - Yanqi Zhou; Nan Du 002; Yanping Huang; Daiyi Peng; Chang Lan; Da Huang; Siamak Shakeri; David R. So; Andrew M. Dai; Yifeng Lu; Zhifeng Chen; Quoc V. Le; et al. (ICML, 2023)

Order Matters in the Presence of Dataset Imbalance for Multilingual Learning. - Dami Choi; Derrick Xin; Hamid Dadkhahi; Justin Gilmer; Ankush Garg; Orhan Firat; Chih-Kuan Yeh; Andrew M. Dai; Behrooz Ghorbani (NeurIPS, 2023)

PaLM 2 Technical Report. - Rohan Anil; Andrew M. Dai; Orhan Firat; Melvin Johnson; Dmitry Lepikhin; Alexandre Passos; Siamak Shakeri; Emanuel Taropa; Paige Bailey; Zhifeng Chen; Eric Chu; Jonathan H. Clark; et al. (CoRR, 2023)

Training Socially Aligned Language Models in Simulated Human Society. - Ruibo Liu; Ruixin Yang; Chenyan Jia; Ge Zhang; Denny Zhou; Andrew M. Dai; Diyi Yang; Soroush Vosoughi (CoRR, 2023)

Gemini: A Family of Highly Capable Multimodal Models. - Rohan Anil; Sebastian Borgeaud; Yonghui Wu; Jean-Baptiste Alayrac; Jiahui Yu; Radu Soricut; Johan Schalkwyk; Andrew M. Dai; Anja Hauth; Katie Millican; David Silver; Slav Petrov; et al. (CoRR, 2023)

PaLM: Scaling Language Modeling with Pathways. - Aakanksha Chowdhery; Sharan Narang; Jacob Devlin; Maarten Bosma; Gaurav Mishra; Adam Roberts; Paul Barham 001; Hyung Won Chung; Charles Sutton; Sebastian Gehrmann; Parker Schuh; Kensen Shi; et al. (J. Mach. Learn. Res., 2023)

MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks. - Weicheng Kuo; A. J. Piergiovanni; Dahun Kim; Xiyang Luo; Benjamin Caine; Wei Li 133; Abhijit S. Ogale; Luowei Zhou; Andrew M. Dai; Zhifeng Chen; Claire Cui; Anelia Angelova (Trans. Mach. Learn. Res., 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. - Machel Reid; Nikolay Savinov; Denis Teplyashin; Dmitry Lepikhin; Timothy P. Lillicrap; Jean-Baptiste Alayrac; Radu Soricut; Angeliki Lazaridou; Orhan Firat; Julian Schrittwieser; Ioannis Antonoglou; Rohan Anil; et al. (CoRR, 2024)

Best Practices and Lessons Learned on Synthetic Data for Language Models. - Ruibo Liu; Jerry Wei; Fangyu Liu; Chenglei Si; Yanzhe Zhang; Jinmeng Rao; Steven Zheng; Daiyi Peng; Diyi Yang; Denny Zhou; Andrew M. Dai (CoRR, 2024)

Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction. - Yuan Xue; Nan Du 002; Anne Mottram; Martin Seneviratne; Andrew M. Dai (CoRR, 2024)

Scaling Instruction-Finetuned Language Models. - Hyung Won Chung; Le Hou; Shayne Longpre; Barret Zoph; Yi Tay; William Fedus; Yunxuan Li; Xuezhi Wang 002; Mostafa Dehghani 001; Siddhartha Brahma; Albert Webson; Shixiang Shane Gu; et al. (J. Mach. Learn. Res., 2024)