Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDAndrew M. Dai
Andrew Mingbo Dai
Person information
- affiliation: Google
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2023
[i39]Ke Hu, Tara N. Sainath, Bo Li, Nan Du, Yanping Huang, Andrew M. Dai, Yu Zhang, Rodrigo Cabrera, Zhifeng Chen, Trevor Strohman:
Massively Multilingual Shallow Fusion with Large Language Models. CoRR abs/2302.08917 (2023)- 2022
- [c27]Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, Quoc V. Le:
Finetuned Language Models are Zero-Shot Learners. ICLR 2022 - [c26]Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten P. Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen S. Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V. Le, Yonghui Wu, Zhifeng Chen, Claire Cui:
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts. ICML 2022: 5547-5569 - [i38]Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Y. Zhao, Andrew M. Dai, Zhifeng Chen, Quoc Le, James Laudon:
Mixture-of-Experts with Expert Choice Routing. CoRR abs/2202.09368 (2022) - [i37]Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, Noah Fiedel:
PaLM: Scaling Language Modeling with Pathways. CoRR abs/2204.02311 (2022) - [i36]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, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Santilli, Andreas Stuhlmüller, Andrew M. Dai, Andrew La, Andrew K. Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakas, et al.:
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. CoRR abs/2206.04615 (2022) - [i35]Ruibo Liu, Jason Wei, Shixiang Shane Gu, Te-Yen Wu, Soroush Vosoughi, Claire Cui, Denny Zhou, Andrew M. Dai:
Mind's Eye: Grounded Language Model Reasoning through Simulation. CoRR abs/2210.05359 (2022) - [i34]Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Y. Zhao, Yanping Huang, Andrew M. Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, Jason Wei:
Scaling Instruction-Finetuned Language Models. CoRR abs/2210.11416 (2022) - 2021
- [c25]Zhen Xu, David R. So, Andrew M. Dai:
MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records. AAAI 2021: 10532-10540 - [c24]Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran:
Training independent subnetworks for robust prediction. ICLR 2021 - [i33]Zhen Xu, David R. So, Andrew M. Dai:
MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records. CoRR abs/2102.02340 (2021) - [i32]Anand Avati, Martin Seneviratne, Emily Xue, Zhen Xu, Balaji Lakshminarayanan, Andrew M. Dai:
BEDS-Bench: Behavior of EHR-models under Distributional Shift-A Benchmark. CoRR abs/2107.08189 (2021) - [i31]Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, Quoc V. Le:
Finetuned Language Models Are Zero-Shot Learners. CoRR abs/2109.01652 (2021) - [i30]Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathy Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V. Le, Yonghui Wu, Zhifeng Chen, Claire Cui:
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts. CoRR abs/2112.06905 (2021) - [i29]Bowen Zhang, Jiahui Yu, Christopher Fifty, Wei Han, Andrew M. Dai, Ruoming Pang, Fei Sha:
Co-training Transformer with Videos and Images Improves Action Recognition. CoRR abs/2112.07175 (2021) - 2020
- [c23]Edward Choi, Zhen Xu, Yujia Li, Michael Dusenberry, Gerardo Flores, Emily Xue, Andrew M. Dai:
Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer. AAAI 2020: 606-613 - [c22]Cinjon Resnick, Abhinav Gupta, Jakob N. Foerster, Andrew M. Dai, Kyunghyun Cho:
Capacity, Bandwidth, and Compositionality in Emergent Language Learning. AAMAS 2020: 1125-1133 - [c21]Michaela Hardt, Alvin Rajkomar, Gerardo Flores, Andrew M. Dai, Michael Howell, Greg Corrado, Claire Cui, Moritz Hardt:
Explaining an increase in predicted risk for clinical alerts. CHIL 2020: 80-89 - [c20]Michael W. Dusenberry, Dustin Tran, Edward Choi
, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine A. Heller, Andrew M. Dai:
Analyzing the role of model uncertainty for electronic health records. CHIL 2020: 204-213 - [c19]Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu:
Flow Contrastive Estimation of Energy-Based Models. CVPR 2020: 7515-7525 - [c18]Yuan Xue, Denny Zhou, Nan Du, Andrew M. Dai, Zhen Xu, Kun Zhang, Claire Cui:
Deep State-Space Generative Model For Correlated Time-to-Event Predictions. KDD 2020: 1552-1562 - [c17]Yuan Xue, Nan Du, Anne Mottram, Martin Seneviratne, Andrew M. Dai:
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction. NeurIPS 2020 - [c16]Abhinav Gupta, Cinjon Resnick, Jakob N. Foerster, Andrew M. Dai, Kyunghyun Cho:
Compositionality and Capacity in Emergent Languages. RepL4NLP@ACL 2020: 34-38 - [i28]Kamil Nar, Yuan Xue, Andrew M. Dai:
Learning Unstable Dynamical Systems with Time-Weighted Logarithmic Loss. CoRR abs/2007.05189 (2020) - [i27]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, Chaitanya Kamath, Dennis Kraft, Akim Kumok, Hinali Marfatia, Yael Mayer, Benjamin Miller, Adam Pearce, Irippuge Milinda Perera, Venky Ramachandran, Karthik Raman, Thomas Roessler, Izhak Shafran, Tomer Shekel, Charlotte Stanton, Jacob Stimes, Mimi Sun, Gregory Wellenius, Masrour Zoghi:
Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0). CoRR abs/2009.01265 (2020) - [i26]Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran:
Training independent subnetworks for robust prediction. CoRR abs/2010.06610 (2020) - [i25]Murphy Yuezhen Niu, Andrew M. Dai, Li Li, Augustus Odena, Zhengli Zhao, Vadim Smelyanskyi, Hartmut Neven, Sergio Boixo:
Learnability and Complexity of Quantum Samples. CoRR abs/2010.11983 (2020)
2010 – 2019
- 2019
- [j2]Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, Slav Petrov:
Natural Questions: a Benchmark for Question Answering Research. Trans. Assoc. Comput. Linguistics 7: 452-466 (2019) - [c15]Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Ian Simon, Curtis Hawthorne, Noam Shazeer, Andrew M. Dai, Matthew D. Hoffman, Monica Dinculescu, Douglas Eck:
Music Transformer: Generating Music with Long-Term Structure. ICLR (Poster) 2019 - [c14]Mia Xu Chen, Benjamin N. Lee, Gagan Bansal, Yuan Cao, Shuyuan Zhang, Justin Lu, Jackie Tsay, Yinan Wang, Andrew M. Dai, Zhifeng Chen, Timothy Sohn, Yonghui Wu:
Gmail Smart Compose: Real-Time Assisted Writing. KDD 2019: 2287-2295 - [i24]Mia Xu Chen, Benjamin N. Lee, Gagan Bansal, Yuan Cao, Shuyuan Zhang, Justin Lu, Jackie Tsay, Yinan Wang, Andrew M. Dai, Zhifeng Chen, Timothy Sohn, Yonghui Wu:
Gmail Smart Compose: Real-Time Assisted Writing. CoRR abs/1906.00080 (2019) - [i23]Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine A. Heller, Andrew M. Dai:
Analyzing the Role of Model Uncertainty for Electronic Health Records. CoRR abs/1906.03842 (2019) - [i22]Edward Choi, Zhen Xu, Yujia Li, Michael W. Dusenberry, Gerardo Flores, Yuan Xue, Andrew M. Dai:
Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records. CoRR abs/1906.04716 (2019) - [i21]Michaela Hardt, Alvin Rajkomar, Gerardo Flores, Andrew M. Dai, Michael Howell, Greg Corrado, Claire Cui, Moritz Hardt:
Explaining an increase in predicted risk for clinical alerts. CoRR abs/1907.04911 (2019) - [i20]Jonas Kemp, Alvin Rajkomar, Andrew M. Dai:
Improved Patient Classification with Language Model Pretraining Over Clinical Notes. CoRR abs/1909.03039 (2019) - [i19]Zhen Xu, Andrew M. Dai, Jonas Kemp, Luke Metz:
Learning an Adaptive Learning Rate Schedule. CoRR abs/1909.09712 (2019) - [i18]Cinjon Resnick, Abhinav Gupta, Jakob N. Foerster, Andrew M. Dai, Kyunghyun Cho:
Capacity, Bandwidth, and Compositionality in Emergent Language Learning. CoRR abs/1910.11424 (2019) - [i17]Stephen R. Pfohl, Andrew M. Dai, Katherine A. Heller:
Federated and Differentially Private Learning for Electronic Health Records. CoRR abs/1911.05861 (2019) - [i16]Kun Zhang, Yuan Xue, Gerardo Flores, Alvin Rajkomar, Claire Cui, Andrew M. Dai:
Modelling EHR timeseries by restricting feature interaction. CoRR abs/1911.06410 (2019) - [i15]Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu:
Flow Contrastive Estimation of Energy-Based Models. CoRR abs/1912.00589 (2019) - [i14]Yuan Xue, Denny Zhou, Nan Du, Andrew M. Dai, Zhen Xu, Kun Zhang, Claire Cui:
Deep Physiological State Space Model for Clinical Forecasting. CoRR abs/1912.01762 (2019) - 2018
- [c13]Melody Y. Guan, Varun Gulshan, Andrew M. Dai, Geoffrey E. Hinton:
Who Said What: Modeling Individual Labelers Improves Classification. AAAI 2018: 3109-3118 - [c12]Wei Wei, Quoc V. Le, Andrew M. Dai, Jia Li:
AirDialogue: An Environment for Goal-Oriented Dialogue Research. EMNLP 2018: 3844-3854 - [c11]William Fedus, Ian J. Goodfellow, Andrew M. Dai:
MaskGAN: Better Text Generation via Filling in the _______. ICLR (Poster) 2018 - [c10]William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian J. Goodfellow:
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step. ICLR (Poster) 2018 - [c9]Trieu H. Trinh, Andrew M. Dai, Minh-Thang Luong, Quoc V. Le:
Learning Longer-term Dependencies in RNNs with Auxiliary Losses. ICLR (Workshop) 2018 - [c8]Trieu H. Trinh, Andrew M. Dai, Thang Luong, Quoc V. Le:
Learning Longer-term Dependencies in RNNs with Auxiliary Losses. ICML 2018: 4972-4981 - [c7]Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl:
Embedding Text in Hyperbolic Spaces. TextGraphs@NAACL-HLT 2018: 59-69 - [i13]William Fedus, Ian J. Goodfellow, Andrew M. Dai:
MaskGAN: Better Text Generation via Filling in the ______. CoRR abs/1801.07736 (2018) - [i12]Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Peter J. Liu, Xiaobing Liu, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Gavin E. Duggan, Gerardo Flores, Michaela Hardt, Jamie Irvine, Quoc V. Le, Kurt Litsch, Jake Marcus, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael Howell, Claire Cui, Greg Corrado, Jeff Dean:
Scalable and accurate deep learning for electronic health records. CoRR abs/1801.07860 (2018) - [i11]Trieu H. Trinh, Andrew M. Dai, Thang Luong, Quoc V. Le:
Learning Longer-term Dependencies in RNNs with Auxiliary Losses. CoRR abs/1803.00144 (2018) - [i10]Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl:
Embedding Text in Hyperbolic Spaces. CoRR abs/1806.04313 (2018) - [i9]Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam Shazeer, Curtis Hawthorne, Andrew M. Dai, Matthew D. Hoffman, Douglas Eck:
An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation. CoRR abs/1809.04281 (2018) - 2017
- [c6]David Ha, Andrew M. Dai, Quoc V. Le:
HyperNetworks. ICLR (Poster) 2017 - [c5]Takeru Miyato, Andrew M. Dai, Ian J. Goodfellow:
Adversarial Training Methods for Semi-Supervised Text Classification. ICLR (Poster) 2017 - [i8]Melody Y. Guan, Varun Gulshan, Andrew M. Dai, Geoffrey E. Hinton:
Who Said What: Modeling Individual Labelers Improves Classification. CoRR abs/1703.08774 (2017) - [i7]William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian J. Goodfellow:
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step. CoRR abs/1710.08446 (2017) - 2016
- [c4]Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, Samy Bengio:
Generating Sentences from a Continuous Space. CoNLL 2016: 10-21 - [i6]Takeru Miyato, Andrew M. Dai, Ian J. Goodfellow:
Virtual Adversarial Training for Semi-Supervised Text Classification. CoRR abs/1605.07725 (2016) - [i5]David Ha, Andrew M. Dai, Quoc V. Le:
HyperNetworks. CoRR abs/1609.09106 (2016) - 2015
- [j1]Andrew M. Dai, Amos J. Storkey:
The Supervised Hierarchical Dirichlet Process. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 243-255 (2015) - [c3]Andrew M. Dai, Quoc V. Le:
Semi-supervised Sequence Learning. NIPS 2015: 3079-3087 - [i4]Andrew M. Dai, Christopher Olah, Quoc V. Le:
Document Embedding with Paragraph Vectors. CoRR abs/1507.07998 (2015) - [i3]Andrew M. Dai, Quoc V. Le:
Semi-supervised Sequence Learning. CoRR abs/1511.01432 (2015) - [i2]Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, Samy Bengio:
Generating Sentences from a Continuous Space. CoRR abs/1511.06349 (2015) - 2014
- [i1]Andrew M. Dai, Amos J. Storkey:
The supervised hierarchical Dirichlet process. CoRR abs/1412.5236 (2014) - 2011
- [c2]Klaus Macherey, Andrew M. Dai, David Talbot, Ashok C. Popat, Franz Josef Och:
Language-independent compound splitting with morphological operations. ACL 2011: 1395-1404 - [c1]Andrew M. Dai, Amos J. Storkey:
The Grouped Author-Topic Model for Unsupervised Entity Resolution. ICANN (1) 2011: 241-249
Coauthor Index
a service of 
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
load content from web.archive.org
Privacy notice: By enabling the option above, your browser will contact the API of web.archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2023-03-14 15:49 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
