Andrew M. Dai

Publication Activity (10 Years)

Years Active: 2011-2023
Publications (10 Years): 78

Top Topics

Continuous Space

Multimodal Fusion

Graphical Structure

Electronic Health Records

Top Venues

Publications

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 (2023)
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. ICLR (2023)
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, Melvin Johnson, Ioannis Antonoglou, Julian Schrittwieser, Amelia Glaese, Jilin Chen, Emily Pitler, Timothy P. Lillicrap, Angeliki Lazaridou, Orhan Firat, James Molloy, Michael Isard, Paul Ronald Barham, Tom Hennigan, Benjamin Lee, Fabio Viola, Malcolm Reynolds, Yuanzhong Xu, Ryan Doherty, Eli Collins, Clemens Meyer, Eliza Rutherford, Erica Moreira, Kareem Ayoub, Megha Goel, George Tucker, Enrique Piqueras, Maxim Krikun, Iain Barr, Nikolay Savinov, Ivo Danihelka, Becca Roelofs, Anaïs White, Anders Andreassen, Tamara von Glehn, Lakshman Yagati, Mehran Kazemi, Lucas Gonzalez, Misha Khalman, Jakub Sygnowski, et al.
Gemini: A Family of Highly Capable Multimodal Models. CoRR (2023)
Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning. NeurIPS (2023)
Weicheng Kuo, AJ Piergiovanni, Dahun Kim, Xiyang Luo, Benjamin Caine, Wei Li, Abhijit S. Ogale, Luowei Zhou, Andrew M. Dai, Zhifeng Chen, Claire Cui, Anelia Angelova
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks. CoRR (2023)
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. J. Mach. Learn. Res. 24 (2023)
Yanqi Zhou, Nan Du, Yanping Huang, Daiyi Peng, Chang Lan, Da Huang, Siamak Shakeri, David R. So, Andrew M. Dai, Yifeng Lu, Zhifeng Chen, Quoc V. Le, Claire Cui, James Laudon, Jeff Dean
Brainformers: Trading Simplicity for Efficiency. ICML (2023)
Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning. CoRR (2023)
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. ICASSP (2023)
Yanqi Zhou, Nan Du, Yanping Huang, Daiyi Peng, Chang Lan, Da Huang, Siamak Shakeri, David R. So, Andrew M. Dai, Yifeng Lu, Zhifeng Chen, Quoc V. Le, Claire Cui, James Laudon, Jeff Dean
Brainformers: Trading Simplicity for Efficiency. CoRR (2023)
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, Laurent El Shafey, Yanping Huang, Kathy Meier-Hellstern, Gaurav Mishra, Erica Moreira, Mark Omernick, Kevin Robinson, Sebastian Ruder, Yi Tay, Kefan Xiao, Yuanzhong Xu, Yujing Zhang, Gustavo Hernández Ábrego, Junwhan Ahn, Jacob Austin, Paul Barham, Jan A. Botha, James Bradbury, Siddhartha Brahma, Kevin Brooks, Michele Catasta, Yong Cheng, Colin Cherry, Christopher A. Choquette-Choo, Aakanksha Chowdhery, Clément Crepy, Shachi Dave, Mostafa Dehghani, Sunipa Dev, Jacob Devlin, Mark Díaz, Nan Du, Ethan Dyer, Vladimir Feinberg, Fangxiaoyu Feng, Vlad Fienber, Markus Freitag, Xavier Garcia, Sebastian Gehrmann, Lucas Gonzalez, et al.
PaLM 2 Technical Report. CoRR (2023)
Ruibo Liu, Ruixin Yang, Chenyan Jia, Ge Zhang, Denny Zhou, Andrew M. Dai, Diyi Yang, Soroush Vosoughi
Training Socially Aligned Language Models in Simulated Human Society. CoRR (2023)
Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Zhao, Andrew M. Dai, Zhifeng Chen, Quoc V. Le, James Laudon
Mixture-of-Experts with Expert Choice Routing. NeurIPS (2022)
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 (2022)
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)
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)
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 (2022)
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 (2022)
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 (2022)
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 (2022)
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 (2021)
Zhen Xu, David R. So, Andrew M. Dai
MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records. CoRR (2021)
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 (2021)
Zhen Xu, David R. So, Andrew M. Dai
MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records. AAAI (2021)
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 (2021)
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 (2021)
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 (2020)
Abhinav Gupta, Cinjon Resnick, Jakob N. Foerster, Andrew M. Dai, Kyunghyun Cho
Compositionality and Capacity in Emergent Languages. RepL4NLP@ACL (2020)
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 (2020)
Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu
Flow Contrastive Estimation of Energy-Based Models. CVPR (2020)
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)
Kamil Nar, Yuan Xue, Andrew M. Dai
Learning Unstable Dynamical Systems with Time-Weighted Logarithmic Loss. CoRR (2020)
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 (2020)
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)
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)
Yuan Xue, Nan Du, Anne Mottram, Martin Seneviratne, Andrew M. Dai
Learning to Select Best Forecast Tasks for Clinical Outcome Prediction. NeurIPS (2020)
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)
Cinjon Resnick, Abhinav Gupta, Jakob N. Foerster, Andrew M. Dai, Kyunghyun Cho
Capacity, Bandwidth, and Compositionality in Emergent Language Learning. AAMAS (2020)
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 (2019)
Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu
Flow Contrastive Estimation of Energy-Based Models. CoRR (2019)
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 (2019)
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 (2019)
Yuan Xue, Denny Zhou, Nan Du, Andrew M. Dai, Zhen Xu, Kun Zhang, Claire Cui
Deep Physiological State Space Model for Clinical Forecasting. CoRR (2019)
Jonas Kemp, Alvin Rajkomar, Andrew M. Dai
Improved Patient Classification with Language Model Pretraining Over Clinical Notes. CoRR (2019)
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 (2019)
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 (2019)
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)
Kun Zhang, Yuan Xue, Gerardo Flores, Alvin Rajkomar, Claire Cui, Andrew M. Dai
Modelling EHR timeseries by restricting feature interaction. CoRR (2019)
Zhen Xu, Andrew M. Dai, Jonas Kemp, Luke Metz
Learning an Adaptive Learning Rate Schedule. CoRR (2019)
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)
Stephen R. Pfohl, Andrew M. Dai, Katherine A. Heller
Federated and Differentially Private Learning for Electronic Health Records. CoRR (2019)
Cinjon Resnick, Abhinav Gupta, Jakob N. Foerster, Andrew M. Dai, Kyunghyun Cho
Capacity, Bandwidth, and Compositionality in Emergent Language Learning. CoRR (2019)
Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Michaela Hardt, Peter J. Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Yi Zhang, Gerardo Flores, Gavin E. Duggan, Jamie Irvine, Quoc Le, Kurt Litsch, 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 D. Howell, Claire Cui, Gregory S. Corrado, Jeffrey Dean
Scalable and accurate deep learning with electronic health records. npj Digit. Medicine 1 (2018)
Trieu H. Trinh, Andrew M. Dai, Thang Luong, Quoc V. Le
Learning Longer-term Dependencies in RNNs with Auxiliary Losses. ICML (2018)
Alvin Rajkomar, Andrew M. Dai, Mimi Sun, Michaela Hardt, Kai Chen, Kathryn Rough, Jeffrey Dean
Reply: metrics to assess machine learning models. npj Digit. Medicine 1 (2018)
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 (2018)
Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl
Embedding Text in Hyperbolic Spaces. CoRR (2018)
Trieu H. Trinh, Andrew M. Dai, Minh-Thang Luong, Quoc V. Le
Learning Longer-term Dependencies in RNNs with Auxiliary Losses. ICLR (Workshop) (2018)
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 (2018)
William Fedus, Ian J. Goodfellow, Andrew M. Dai
MaskGAN: Better Text Generation via Filling in the ______. CoRR (2018)
Wei Wei, Quoc V. Le, Andrew M. Dai, Jia Li
AirDialogue: An Environment for Goal-Oriented Dialogue Research. EMNLP (2018)
Trieu H. Trinh, Andrew M. Dai, Thang Luong, Quoc V. Le
Learning Longer-term Dependencies in RNNs with Auxiliary Losses. CoRR (2018)
Melody Y. Guan, Varun Gulshan, Andrew M. Dai, Geoffrey E. Hinton
Who Said What: Modeling Individual Labelers Improves Classification. AAAI (2018)
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)
William Fedus, Ian J. Goodfellow, Andrew M. Dai
MaskGAN: Better Text Generation via Filling in the _______. ICLR (Poster) (2018)
Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl
Embedding Text in Hyperbolic Spaces. TextGraphs@NAACL-HLT (2018)
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 (2017)
David Ha, Andrew M. Dai, Quoc V. Le
HyperNetworks. ICLR (Poster) (2017)
Melody Y. Guan, Varun Gulshan, Andrew M. Dai, Geoffrey E. Hinton
Who Said What: Modeling Individual Labelers Improves Classification. CoRR (2017)
Takeru Miyato, Andrew M. Dai, Ian J. Goodfellow
Adversarial Training Methods for Semi-Supervised Text Classification. ICLR (Poster) (2017)
Takeru Miyato, Andrew M. Dai, Ian J. Goodfellow
Virtual Adversarial Training for Semi-Supervised Text Classification. CoRR (2016)
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, Samy Bengio
Generating Sentences from a Continuous Space. CoNLL (2016)
David Ha, Andrew M. Dai, Quoc V. Le
HyperNetworks. CoRR (2016)
Andrew M. Dai, Quoc V. Le
Semi-supervised Sequence Learning. CoRR (2015)
Andrew M. Dai, Quoc V. Le
Semi-supervised Sequence Learning. NIPS (2015)
Andrew M. Dai, Christopher Olah, Quoc V. Le
Document Embedding with Paragraph Vectors. CoRR (2015)
Andrew M. Dai, Amos J. Storkey
The Supervised Hierarchical Dirichlet Process. IEEE Trans. Pattern Anal. Mach. Intell. 37 (2) (2015)
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, Samy Bengio
Generating Sentences from a Continuous Space. CoRR (2015)
Andrew M. Dai, Amos J. Storkey
The supervised hierarchical Dirichlet process. CoRR (2014)
Klaus Macherey, Andrew M. Dai, David Talbot, Ashok C. Popat, Franz Josef Och
Language-independent compound splitting with morphological operations. ACL (2011)
Andrew M. Dai, Amos J. Storkey
The Grouped Author-Topic Model for Unsupervised Entity Resolution. ICANN (1) (2011)