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ORCIDQuoc V. Le
Quoc Viet Le – Quoc Le 0001
Person information
- affiliation: Google Inc., Mountain View, CA, USA
- affiliation: Stanford University, Computer Science Department, CA, USA
Other persons with the same name
- Quoc Le 0002 — Santa Clara University, School of Engineering, Department of Computer Engineering, CA, USA
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2020 – today
- 2024
[j14]Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, 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. J. Mach. Learn. Res. 25: 70:1-70:53 (2024)- [j13]Trieu H. Trinh
, Yuhuai Wu, Quoc V. Le, He He, Thang Luong:
Solving olympiad geometry without human demonstrations. Nat. 625(7995): 476-482 (2024) - [c158]Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry W. Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc V. Le, Thang Luong:
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation. ACL (Findings) 2024: 13697-13720 - [c157]Xiao Ma, Swaroop Mishra, Ariel Liu, Sophie Ying Su, Jilin Chen, Chinmay Kulkarni, Heng-Tze Cheng, Quoc V. Le, Ed H. Chi:
Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses. CHI Extended Abstracts 2024: 56:1-56:12 - [c156]Zhecan Wang, Garrett Bingham, Adams Wei Yu, Quoc V. Le, Thang Luong, Golnaz Ghiasi:
HaloQuest: A Visual Hallucination Dataset for Advancing Multimodal Reasoning. ECCV (77) 2024: 288-304 - [c155]Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, Xinyun Chen:
Large Language Models as Optimizers. ICLR 2024 - [c154]Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen, Heng-Tze Cheng, Ed H. Chi, Quoc V. Le, Denny Zhou:
Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models. ICLR 2024 - [i159]Pei Zhou, Jay Pujara, Xiang Ren, Xinyun Chen, Heng-Tze Cheng, Quoc V. Le, Ed H. Chi, Denny Zhou, Swaroop Mishra, Huaixiu Steven Zheng:
Self-Discover: Large Language Models Self-Compose Reasoning Structures. CoRR abs/2402.03620 (2024) - [i158]Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, Quoc V. Le:
Long-form factuality in large language models. CoRR abs/2403.18802 (2024) - [i157]Huaixiu Steven Zheng, Swaroop Mishra, Hugh Zhang, Xinyun Chen, Minmin Chen, Azade Nova, Le Hou, Heng-Tze Cheng, Quoc V. Le, Ed H. Chi, Denny Zhou:
NATURAL PLAN: Benchmarking LLMs on Natural Language Planning. CoRR abs/2406.04520 (2024) - [i156]Zhecan Wang, Garrett Bingham, Adams Yu, Quoc V. Le, Thang Luong, Golnaz Ghiasi:
HaloQuest: A Visual Hallucination Dataset for Advancing Multimodal Reasoning. CoRR abs/2407.15680 (2024) - [i155]Bradley C. A. Brown, Jordan Juravsky, Ryan Saul Ehrlich, Ronald Clark, Quoc V. Le, Christopher Ré, Azalia Mirhoseini:
Large Language Monkeys: Scaling Inference Compute with Repeated Sampling. CoRR abs/2407.21787 (2024) - [i154]Kiran Vodrahalli, Santiago Ontanon, Nilesh Tripuraneni, Kelvin Xu, Sanil Jain, Rakesh Shivanna, Jeffrey Hui, Nishanth Dikkala, Mehran Kazemi, Bahare Fatemi, Rohan Anil, Ethan Dyer, Siamak Shakeri, Roopali Vij, Harsh Mehta, Vinay V. Ramasesh, Quoc Le, Ed H. Chi, Yifeng Lu, Orhan Firat, Angeliki Lazaridou, Jean-Baptiste Lespiau, Nithya Attaluri, Kate Olszewska:
Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries. CoRR abs/2409.12640 (2024) - 2023
- [j12]Hieu Pham, Zihang Dai, Golnaz Ghiasi, Kenji Kawaguchi, Hanxiao Liu, Adams Wei Yu, Jiahui Yu, Yi-Ting Chen, Minh-Thang Luong, Yonghui Wu, Mingxing Tan, Quoc V. Le:
Combined scaling for zero-shot transfer learning. Neurocomputing 555: 126658 (2023) - [c153]Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed H. Chi, Denny Zhou, Jason Wei:
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them. ACL (Findings) 2023: 13003-13051 - [c152]Sheng Li, Garrett Andersen, Tao Chen, Liqun Cheng, Julian Grady, Da Huang, Quoc V. Le, Andrew Li, Xin Li, Yang Li, Chen Liang, Yifeng Lu, Yun Ni, Ruoming Pang, Mingxing Tan, Martin Wicke, Gang Wu, Shengqi Zhu, Parthasarathy Ranganathan, Norman P. Jouppi:
Hyperscale Hardware Optimized Neural Architecture Search. ASPLOS (3) 2023: 343-358 - [c151]Jerry W. Wei, Le Hou, Andrew K. Lampinen, Xiangning Chen, Da Huang, Yi Tay, Xinyun Chen, Yifeng Lu, Denny Zhou, Tengyu Ma, Quoc V. Le:
Symbol tuning improves in-context learning in language models. EMNLP 2023: 968-979 - [c150]Yi Tay, Jason Wei, Hyung Won Chung, Vinh Q. Tran, David R. So, Siamak Shakeri, Xavier Garcia, Huaixiu Steven Zheng, Jinfeng Rao, Aakanksha Chowdhery, Denny Zhou, Donald Metzler, Slav Petrov, Neil Houlsby, Quoc V. Le, Mostafa Dehghani:
Transcending Scaling Laws with 0.1% Extra Compute. EMNLP 2023: 1471-1486 - [c149]Jason Wei, Najoung Kim, Yi Tay, Quoc V. Le:
Inverse Scaling Can Become U-Shaped. EMNLP 2023: 15580-15591 - [c148]Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, Denny Zhou:
Self-Consistency Improves Chain of Thought Reasoning in Language Models. ICLR 2023 - [c147]Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, Ed H. Chi:
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. ICLR 2023 - [c146]Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V. Le, Barret Zoph, Jason Wei, Adam Roberts:
The Flan Collection: Designing Data and Methods for Effective Instruction Tuning. ICML 2023: 22631-22648 - [c145]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: 42531-42542 - [c144]Xiangning Chen, Chen Liang, Da Huang, Esteban Real, Kaiyuan Wang, Hieu Pham, Xuanyi Dong, Thang Luong, Cho-Jui Hsieh, Yifeng Lu, Quoc V. Le:
Symbolic Discovery of Optimization Algorithms. NeurIPS 2023 - [c143]Sang Michael Xie, Hieu Pham, Xuanyi Dong, Nan Du, Hanxiao Liu, Yifeng Lu, Percy Liang, Quoc V. Le, Tengyu Ma, Adams Wei Yu:
DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining. NeurIPS 2023 - [i153]Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V. Le, Barret Zoph, Jason Wei, Adam Roberts:
The Flan Collection: Designing Data and Methods for Effective Instruction Tuning. CoRR abs/2301.13688 (2023) - [i152]Daiyi Peng, Xuanyi Dong, Esteban Real, Yifeng Lu, Quoc V. Le:
PyGlove: Efficiently Exchanging ML Ideas as Code. CoRR abs/2302.01918 (2023) - [i151]Qingqing Huang, Daniel S. Park, Tao Wang, Timo I. Denk, Andy Ly, Nanxin Chen, Zhengdong Zhang, Zhishuai Zhang, Jiahui Yu, Christian Havnø Frank, Jesse H. Engel, Quoc V. Le, William Chan, Wei Han:
Noise2Music: Text-conditioned Music Generation with Diffusion Models. CoRR abs/2302.03917 (2023) - [i150]Ryan Gillard, Stephen Jonany, Yingjie Miao, Michael Munn, Connal de Souza, Jonathan Dungay, Chen Liang, David R. So, Quoc V. Le, Esteban Real:
Unified Functional Hashing in Automatic Machine Learning. CoRR abs/2302.05433 (2023) - [i149]Xiangning Chen, Chen Liang, Da Huang, Esteban Real, Kaiyuan Wang, Yao Liu, Hieu Pham, Xuanyi Dong, Thang Luong, Cho-Jui Hsieh, Yifeng Lu, Quoc V. Le:
Symbolic Discovery of Optimization Algorithms. CoRR abs/2302.06675 (2023) - [i148]Jerry W. Wei, Le Hou, Andrew K. Lampinen, Xiangning Chen, Da Huang, Yi Tay, Xinyun Chen, Yifeng Lu, Denny Zhou, Tengyu Ma, Quoc V. Le:
Symbol tuning improves in-context learning in language models. CoRR abs/2305.08298 (2023) - [i147]Sang Michael Xie, Hieu Pham, Xuanyi Dong, Nan Du, Hanxiao Liu, Yifeng Lu, Percy Liang, Quoc V. Le, Tengyu Ma, Adams Wei Yu:
DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining. CoRR abs/2305.10429 (2023) - [i146]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 abs/2306.00008 (2023) - [i145]Jordan Dotzel, Gang Wu, Andrew Li, Muhammad Umar, Yun Ni, Mohamed S. Abdelfattah, Zhiru Zhang, Liqun Cheng, Martin G. Dixon, Norman P. Jouppi, Quoc V. Le, Sheng Li:
FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search. CoRR abs/2308.03290 (2023) - [i144]Jerry W. Wei, Da Huang, Yifeng Lu, Denny Zhou, Quoc V. Le:
Simple synthetic data reduces sycophancy in large language models. CoRR abs/2308.03958 (2023) - [i143]Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, Xinyun Chen:
Large Language Models as Optimizers. CoRR abs/2309.03409 (2023) - [i142]Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry W. Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc V. Le, Thang Luong:
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation. CoRR abs/2310.03214 (2023) - [i141]Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen, Heng-Tze Cheng, Ed H. Chi, Quoc V. Le, Denny Zhou:
Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models. CoRR abs/2310.06117 (2023) - [i140]Xiao Ma, Swaroop Mishra, Ariel Liu, Sophie Ying Su, Jilin Chen, Chinmay Kulkarni, Heng-Tze Cheng, Quoc V. Le, Ed H. Chi:
Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses. CoRR abs/2312.00763 (2023) - [i139]Esteban Real, Yao Chen, Mirko Rossini, Connal de Souza, Manav Garg, Akhil Verghese, Moritz Firsching, Quoc V. Le, Ekin Dogus Cubuk, David H. Park:
AutoNumerics-Zero: Automated Discovery of State-of-the-Art Mathematical Functions. CoRR abs/2312.08472 (2023) - 2022
- [j11]David A. Patterson, Joseph Gonzalez, Urs Hölzle, Quoc V. Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David R. So, Maud Texier, Jeff Dean:
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. Computer 55(7): 18-28 (2022) - [j10]Yu Zhang, Daniel S. Park, Wei Han, James Qin, Anmol Gulati, Joel Shor, Aren Jansen, Yuanzhong Xu, Yanping Huang, Shibo Wang, Zongwei Zhou, Bo Li, Min Ma, William Chan, Jiahui Yu, Yongqiang Wang, Liangliang Cao, Khe Chai Sim, Bhuvana Ramabhadran, Tara N. Sainath, Françoise Beaufays, Zhifeng Chen, Quoc V. Le, Chung-Cheng Chiu, Ruoming Pang, Yonghui Wu:
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition. IEEE J. Sel. Top. Signal Process. 16(6): 1519-1532 (2022) - [c142]Dan Zhang, Safeen Huda, Ebrahim M. Songhori, Kartik Prabhu, Quoc V. Le, Anna Goldie, Azalia Mirhoseini:
A full-stack search technique for domain optimized deep learning accelerators. ASPLOS 2022: 27-42 - [c141]Yingwei Li, Adams Wei Yu, Tianjian Meng, Benjamin Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Yifeng Lu, Denny Zhou, Quoc V. Le, Alan L. Yuille, Mingxing Tan:
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection. CVPR 2022: 17161-17170 - [c140]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 - [c139]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 - [c138]Weizhe Hua, Zihang Dai, Hanxiao Liu, Quoc V. Le:
Transformer Quality in Linear Time. ICML 2022: 9099-9117 - [c137]Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, Denny Zhou:
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. NeurIPS 2022 - [c136]Chengrun Yang, Gabriel Bender, Hanxiao Liu, Pieter-Jan Kindermans, Madeleine Udell, Yifeng Lu, Quoc V. Le, Da Huang:
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets. NeurIPS 2022 - [c135]Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Y. Zhao, Andrew M. Dai, Zhifeng Chen, Quoc V. Le, James Laudon:
Mixture-of-Experts with Expert Choice Routing. NeurIPS 2022 - [c134]Gary Wang, Ekin D. Cubuk, Andrew Rosenberg, Shuyang Cheng, Ron J. Weiss, Bhuvana Ramabhadran, Pedro J. Moreno, Quoc V. Le, Daniel S. Park:
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR. SLT 2022: 23-30 - [i138]Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Kathleen S. Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Agüera y Arcas, Claire Cui, Marian Croak, Ed H. Chi, Quoc Le:
LaMDA: Language Models for Dialog Applications. CoRR abs/2201.08239 (2022) - [i137]Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed H. Chi, Quoc Le, Denny Zhou:
Chain of Thought Prompting Elicits Reasoning in Large Language Models. CoRR abs/2201.11903 (2022) - [i136]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) - [i135]Weizhe Hua, Zihang Dai, Hanxiao Liu, Quoc V. Le:
Transformer Quality in Linear Time. CoRR abs/2202.10447 (2022) - [i134]Yingwei Li, Adams Wei Yu, Tianjian Meng, Benjamin Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Bo Wu, Yifeng Lu, Denny Zhou, Quoc V. Le, Alan L. Yuille, Mingxing Tan:
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection. CoRR abs/2203.08195 (2022) - [i133]Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Denny Zhou:
Self-Consistency Improves Chain of Thought Reasoning in Language Models. CoRR abs/2203.11171 (2022) - [i132]Tianjian Meng, Golnaz Ghiasi, Reza Mahjourian, Quoc V. Le, Mingxing Tan:
Revisiting Multi-Scale Feature Fusion for Semantic Segmentation. CoRR abs/2203.12683 (2022) - [i131]David A. Patterson, Joseph Gonzalez, Urs Hölzle, Quoc V. Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David R. So, Maud Texier, Jeff Dean:
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. CoRR abs/2204.05149 (2022) - [i130]Chengrun Yang, Gabriel Bender, Hanxiao Liu, Pieter-Jan Kindermans, Madeleine Udell, Yifeng Lu, Quoc V. Le, Da Huang:
Resource-Constrained Neural Architecture Search on Tabular Datasets. CoRR abs/2204.07615 (2022) - [i129]Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Olivier Bousquet, Quoc Le, Ed H. Chi:
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. CoRR abs/2205.10625 (2022) - [i128]Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Denny Zhou:
Rationale-Augmented Ensembles in Language Models. CoRR abs/2207.00747 (2022) - [i127]Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed H. Chi, Denny Zhou, Jason Wei:
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them. CoRR abs/2210.09261 (2022) - [i126]Gary Wang, Ekin D. Cubuk, Andrew Rosenberg, Shuyang Cheng, Ron J. Weiss, Bhuvana Ramabhadran, Pedro J. Moreno, Quoc V. Le, Daniel S. Park:
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR. CoRR abs/2210.10879 (2022) - [i125]Yi Tay, Jason Wei, Hyung Won Chung, Vinh Q. Tran, David R. So, Siamak Shakeri, Xavier Garcia, Huaixiu Steven Zheng, Jinfeng Rao, Aakanksha Chowdhery, Denny Zhou, Donald Metzler, Slav Petrov, Neil Houlsby, Quoc V. Le, Mostafa Dehghani:
Transcending Scaling Laws with 0.1% Extra Compute. CoRR abs/2210.11399 (2022) - [i124]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) - [i123]Jason Wei, Yi Tay, Quoc V. Le:
Inverse scaling can become U-shaped. CoRR abs/2211.02011 (2022) - 2021
- [j9]Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan, Joe Wenjie Jiang, Ebrahim M. Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Omkar Pathak, Azade Nazi, Jiwoo Pak, Andy Tong, Kavya Srinivasa, William Hang, Emre Tuncer, Quoc V. Le, James Laudon, Richard Ho, Roger Carpenter, Jeff Dean:
A graph placement methodology for fast chip design. Nat. 594(7862): 207-212 (2021) - [c133]Hieu Pham, Quoc V. Le:
AutoDropout: Learning Dropout Patterns to Regularize Deep Networks. AAAI 2021: 9351-9359 - [c132]Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D. Cubuk, Quoc V. Le, Barret Zoph:
Simple Copy-Paste Is a Strong Data Augmentation Method for Instance Segmentation. CVPR 2021: 2918-2928 - [c131]Sheng Li, Mingxing Tan, Ruoming Pang, Andrew Li, Liqun Cheng, Quoc V. Le, Norman P. Jouppi:
Searching for Fast Model Families on Datacenter Accelerators. CVPR 2021: 8085-8095 - [c130]Hieu Pham, Zihang Dai, Qizhe Xie, Quoc V. Le:
Meta Pseudo Labels. CVPR 2021: 11557-11568 - [c129]Tu Vu, Minh-Thang Luong, Quoc V. Le, Grady Simon, Mohit Iyyer:
STraTA: Self-Training with Task Augmentation for Better Few-shot Learning. EMNLP (1) 2021: 5715-5731 - [c128]Golnaz Ghiasi, Barret Zoph, Ekin D. Cubuk, Quoc V. Le, Tsung-Yi Lin:
Multi-Task Self-Training for Learning General Representations. ICCV 2021: 8836-8845 - [c127]John D. Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Quoc V. Le, Sergey Levine, Honglak Lee, Aleksandra Faust:
Evolving Reinforcement Learning Algorithms. ICLR 2021 - [c126]Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yun-Hsuan Sung, Zhen Li, Tom Duerig:
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision. ICML 2021: 4904-4916 - [c125]Mingxing Tan, Quoc V. Le:
EfficientNetV2: Smaller Models and Faster Training. ICML 2021: 10096-10106 - [c124]Vikas Verma, Thang Luong, Kenji Kawaguchi, Hieu Pham, Quoc V. Le:
Towards Domain-Agnostic Contrastive Learning. ICML 2021: 10530-10541 - [c123]Arissa Wongpanich, Hieu Pham, James Demmel, Mingxing Tan, Quoc V. Le, Yang You, Sameer Kumar:
Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour. IPDPS Workshops 2021: 947-950 - [c122]Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan:
CoAtNet: Marrying Convolution and Attention for All Data Sizes. NeurIPS 2021: 3965-3977 - [c121]David R. So, Wojciech Manke, Hanxiao Liu, Zihang Dai, Noam Shazeer, Quoc V. Le:
Searching for Efficient Transformers for Language Modeling. NeurIPS 2021: 6010-6022 - [c120]Hanxiao Liu, Zihang Dai, David R. So, Quoc V. Le:
Pay Attention to MLPs. NeurIPS 2021: 9204-9215 - [i122]Hieu Pham, Quoc V. Le:
AutoDropout: Learning Dropout Patterns to Regularize Deep Networks. CoRR abs/2101.01761 (2021) - [i121]John D. Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Sergey Levine, Quoc V. Le, Honglak Lee, Aleksandra Faust:
Evolving Reinforcement Learning Algorithms. CoRR abs/2101.03958 (2021) - [i120]Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Hanxiao Liu, Gabriel Bender, Adam Kraft, Chen Liang, Quoc V. Le:
PyGlove: Symbolic Programming for Automated Machine Learning. CoRR abs/2101.08809 (2021) - [i119]Sheng Li, Mingxing Tan, Ruoming Pang, Andrew Li, Liqun Cheng, Quoc V. Le, Norman P. Jouppi:
Searching for Fast Model Families on Datacenter Accelerators. CoRR abs/2102.05610 (2021) - [i118]Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yun-Hsuan Sung, Zhen Li, Tom Duerig:
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision. CoRR abs/2102.05918 (2021) - [i117]Mingxing Tan, Quoc V. Le:
EfficientNetV2: Smaller Models and Faster Training. CoRR abs/2104.00298 (2021) - [i116]William Chan, Daniel S. Park, Chris A. Lee, Yu Zhang, Quoc V. Le, Mohammad Norouzi:
SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network. CoRR abs/2104.02133 (2021) - [i115]David A. Patterson, Joseph Gonzalez, Quoc V. Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David R. So, Maud Texier, Jeff Dean:
Carbon Emissions and Large Neural Network Training. CoRR abs/2104.10350 (2021) - [i114]Hanxiao Liu, Zihang Dai, David R. So, Quoc V. Le:
Pay Attention to MLPs. CoRR abs/2105.08050 (2021) - [i113]Dan Zhang, Safeen Huda, Ebrahim M. Songhori, Quoc V. Le, Anna Goldie, Azalia Mirhoseini:
A Full-stack Accelerator Search Technique for Vision Applications. CoRR abs/2105.12842 (2021) - [i112]Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan:
CoAtNet: Marrying Convolution and Attention for All Data Sizes. CoRR abs/2106.04803 (2021) - [i111]Jacob Austin, Augustus Odena, Maxwell I. Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie J. Cai, Michael Terry, Quoc V. Le, Charles Sutton:
Program Synthesis with Large Language Models. CoRR abs/2108.07732 (2021) - [i110]Golnaz Ghiasi, Barret Zoph, Ekin D. Cubuk, Quoc V. Le, Tsung-Yi Lin:
Multi-Task Self-Training for Learning General Representations. CoRR abs/2108.11353 (2021) - [i109]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) - [i108]Tu Vu, Minh-Thang Luong, Quoc V. Le, Grady Simon, Mohit Iyyer:
STraTA: Self-Training with Task Augmentation for Better Few-shot Learning. CoRR abs/2109.06270 (2021) - [i107]David R. So, Wojciech Manke, Hanxiao Liu, Zihang Dai, Noam Shazeer, Quoc V. Le:
Primer: Searching for Efficient Transformers for Language Modeling. CoRR abs/2109.08668 (2021) - [i106]Yu Zhang, Daniel S. Park, Wei Han, James Qin, Anmol Gulati, Joel Shor, Aren Jansen, Yuanzhong Xu, Yanping Huang, Shibo Wang, Zongwei Zhou, Bo Li, Min Ma, William Chan, Jiahui Yu, Yongqiang Wang, Liangliang Cao, Khe Chai Sim, Bhuvana Ramabhadran, Tara N. Sainath, Françoise Beaufays, Zhifeng Chen, Quoc V. Le, Chung-Cheng Chiu, Ruoming Pang, Yonghui Wu:
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition. CoRR abs/2109.13226 (2021) - [i105]Hieu Pham, Zihang Dai, Golnaz Ghiasi, Hanxiao Liu, Adams Wei Yu, Minh-Thang Luong, Mingxing Tan, Quoc V. Le:
Combined Scaling for Zero-shot Transfer Learning. CoRR abs/2111.10050 (2021) - [i104]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) - 2020
- [c119]Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, Quoc V. Le:
Adversarial Examples Improve Image Recognition. CVPR 2020: 816-825 - [c118]Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, Quoc V. Le:
Randaugment: Practical automated data augmentation with a reduced search space. CVPR Workshops 2020: 3008-3017 - [c117]Qizhe Xie, Minh-Thang Luong, Eduard H. Hovy, Quoc V. Le:
Self-Training With Noisy Student Improves ImageNet Classification. CVPR 2020: 10684-10695 - [c116]Mingxing Tan, Ruoming Pang, Quoc V. Le:
EfficientDet: Scalable and Efficient Object Detection. CVPR 2020: 10778-10787 - [c115]Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Golnaz Ghiasi, Mingxing Tan, Yin Cui, Quoc V. Le, Xiaodan Song:
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization. CVPR 2020: 11589-11598 - [c114]Bo Chen, Golnaz Ghiasi, Hanxiao Liu, Tsung-Yi Lin, Dmitry Kalenichenko, Hartwig Adam, Quoc V. Le:
MnasFPN: Learning Latency-Aware Pyramid Architecture for Object Detection on Mobile Devices. CVPR 2020: 13604-13613 - [c113]Gabriel Bender, Hanxiao Liu, Bo Chen, Grace Chu, Shuyang Cheng, Pieter-Jan Kindermans, Quoc V. Le:
Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS. CVPR 2020: 14311-14320 - [c112]Shuyang Cheng, Zhaoqi Leng, Ekin Dogus Cubuk, Barret Zoph, Chunyan Bai, Jiquan Ngiam, Yang Song, Benjamin Caine, Vijay Vasudevan, Congcong Li, Quoc V. Le, Jonathon Shlens, Dragomir Anguelov:
Improving 3D Object Detection Through Progressive Population Based Augmentation. ECCV (21) 2020: 279-294 - [c111]Barret Zoph, Ekin D. Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, Quoc V. Le:
Learning Data Augmentation Strategies for Object Detection. ECCV (27) 2020: 566-583 - [c110]Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Yin Cui, Mingxing Tan, Quoc V. Le, Xiaodan Song:
Efficient Scale-Permuted Backbone with Learned Resource Distribution. ECCV (23) 2020: 572-586 - [c109]Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas S. Huang, Xiaodan Song, Ruoming Pang, Quoc Le:
BigNAS: Scaling up Neural Architecture Search with Big Single-Stage Models. ECCV (7) 2020: 702-717 - [c108]Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning:
Pre-Training Transformers as Energy-Based Cloze Models. EMNLP (1) 2020: 285-294 - [c107]Daniel S. Park, Yu Zhang, Chung-Cheng Chiu, Youzheng Chen, Bo Li, William Chan, Quoc V. Le, Yonghui Wu:
Specaugment on Large Scale Datasets. ICASSP 2020: 6879-6883 - [c106]Xinyun Chen, Chen Liang, Adams Wei Yu, Denny Zhou, Dawn Song, Quoc V. Le:
Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension. ICLR 2020 - [c105]Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning:
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. ICLR 2020 - [c104]Esteban Real, Chen Liang, David R. So, Quoc V. Le:
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch. ICML 2020: 8007-8019 - [c103]Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans:
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. ICML 2020: 11546-11555 - [c102]Daniel S. Park, Yu Zhang, Ye Jia, Wei Han, Chung-Cheng Chiu, Bo Li, Yonghui Wu, Quoc V. Le:
Improved Noisy Student Training for Automatic Speech Recognition. INTERSPEECH 2020: 2817-2821 - [c101]Manas R. Joglekar, Cong Li, Mei Chen, Taibai Xu, Xiaoming Wang, Jay K. Adams, Pranav Khaitan, Jiahui Liu, Quoc V. Le:
Neural Input Search for Large Scale Recommendation Models. KDD 2020: 2387-2397 - [c100]Ekin Dogus Cubuk, Barret Zoph, Jonathon Shlens, Quoc Le:
RandAugment: Practical Automated Data Augmentation with a Reduced Search Space. NeurIPS 2020 - [c99]Zihang Dai, Guokun Lai, Yiming Yang, Quoc Le:
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing. NeurIPS 2020 - [c98]Hanxiao Liu, Andy Brock, Karen Simonyan, Quoc Le:
Evolving Normalization-Activation Layers. NeurIPS 2020 - [c97]Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Gabriel Bender, Hanxiao Liu, Adam Kraft, Chen Liang, Quoc Le:
PyGlove: Symbolic Programming for Automated Machine Learning. NeurIPS 2020 - [c96]Qizhe Xie, Zihang Dai, Eduard H. Hovy, Thang Luong, Quoc Le:
Unsupervised Data Augmentation for Consistency Training. NeurIPS 2020 - [c95]Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, Quoc Le:
Rethinking Pre-training and Self-training. NeurIPS 2020 - [i103]Daniel Adiwardana, Minh-Thang Luong, David R. So, Jamie Hall, Noah Fiedel, Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, Quoc V. Le:
Towards a Human-like Open-Domain Chatbot. CoRR abs/2001.09977 (2020) - [i102]Esteban Real, Chen Liang, David R. So, Quoc V. Le:
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch. CoRR abs/2003.03384 (2020) - [i101]Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning:
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. CoRR abs/2003.10555 (2020) - [i100]Hieu Pham, Qizhe Xie, Zihang Dai, Quoc V. Le:
Meta Pseudo Labels. CoRR abs/2003.10580 (2020) - [i99]Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas S. Huang, Xiaodan Song, Ruoming Pang, Quoc V. Le:
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models. CoRR abs/2003.11142 (2020) - [i98]Shuyang Cheng, Zhaoqi Leng, Ekin Dogus Cubuk, Barret Zoph, Chunyan Bai, Jiquan Ngiam, Yang Song, Benjamin Caine, Vijay Vasudevan, Congcong Li, Quoc V. Le, Jonathon Shlens, Dragomir Anguelov:
Improving 3D Object Detection through Progressive Population Based Augmentation. CoRR abs/2004.00831 (2020) - [i97]Hanxiao Liu, Andrew Brock, Karen Simonyan, Quoc V. Le:
Evolving Normalization-Activation Layers. CoRR abs/2004.02967 (2020) - [i96]Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan, Joe W. J. Jiang, Ebrahim M. Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Omkar Pathak, Sungmin Bae, Azade Nazi, Jiwoo Pak, Andy Tong, Kavya Srinivasa, William Hang, Emre Tuncer, Anand Babu, Quoc V. Le, James Laudon, Richard Ho, Roger Carpenter, Jeff Dean:
Chip Placement with Deep Reinforcement Learning. CoRR abs/2004.10746 (2020) - [i95]Daniel S. Park, Yu Zhang, Ye Jia, Wei Han, Chung-Cheng Chiu, Bo Li, Yonghui Wu, Quoc V. Le:
Improved Noisy Student Training for Automatic Speech Recognition. CoRR abs/2005.09629 (2020) - [i94]Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le:
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing. CoRR abs/2006.03236 (2020) - [i93]Xuanyi Dong, Mingxing Tan, Adams Wei Yu, Daiyi Peng, Bogdan Gabrys, Quoc V. Le:
AutoHAS: Differentiable Hyper-parameter and Architecture Search. CoRR abs/2006.03656 (2020) - [i92]Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin D. Cubuk, Quoc V. Le:
Rethinking Pre-training and Self-training. CoRR abs/2006.06882 (2020) - [i91]Cihang Xie, Mingxing Tan, Boqing Gong, Alan L. Yuille, Quoc V. Le:
Smooth Adversarial Training. CoRR abs/2006.14536 (2020) - [i90]Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans:
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. CoRR abs/2007.00811 (2020) - [i89]Gabriel Bender, Hanxiao Liu, Bo Chen, Grace Chu, Shuyang Cheng, Pieter-Jan Kindermans, Quoc Le:
Can weight sharing outperform random architecture search? An investigation with TuNAS. CoRR abs/2008.06120 (2020) - [i88]Yu Zhang, James Qin, Daniel S. Park, Wei Han, Chung-Cheng Chiu, Ruoming Pang, Quoc V. Le, Yonghui Wu:
Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition. CoRR abs/2010.10504 (2020) - [i87]Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Yin Cui, Mingxing Tan, Quoc V. Le, Xiaodan Song:
Efficient Scale-Permuted Backbone with Learned Resource Distribution. CoRR abs/2010.11426 (2020) - [i86]Arissa Wongpanich, Hieu Pham, James Demmel, Mingxing Tan, Quoc V. Le, Yang You, Sameer Kumar:
Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour. CoRR abs/2011.00071 (2020) - [i85]Vikas Verma, Minh-Thang Luong, Kenji Kawaguchi, Hieu Pham, Quoc V. Le:
Towards Domain-Agnostic Contrastive Learning. CoRR abs/2011.04419 (2020) - [i84]Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D. Cubuk, Quoc V. Le, Barret Zoph:
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation. CoRR abs/2012.07177 (2020) - [i83]Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning:
Pre-Training Transformers as Energy-Based Cloze Models. CoRR abs/2012.08561 (2020)
2010 – 2019
- 2019
- [j8]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) - [c94]Esteban Real, Alok Aggarwal, Yanping Huang, Quoc V. Le:
Regularized Evolution for Image Classifier Architecture Search. AAAI 2019: 4780-4789 - [c93]Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G. Carbonell, Quoc Viet Le, Ruslan Salakhutdinov:
Transformer-XL: Attentive Language Models beyond a Fixed-Length Context. ACL (1) 2019: 2978-2988 - [c92]Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, Christopher D. Manning, Quoc V. Le:
BAM! Born-Again Multi-Task Networks for Natural Language Understanding. ACL (1) 2019: 5931-5937 - [c91]Mingxing Tan, Quoc V. Le:
MixConv: Mixed Depthwise Convolutional Kernels. BMVC 2019: 74 - [c90]Ekin D. Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, Quoc V. Le:
AutoAugment: Learning Augmentation Strategies From Data. CVPR 2019: 113-123 - [c89]Simon Kornblith, Jonathon Shlens, Quoc V. Le:
Do Better ImageNet Models Transfer Better? CVPR 2019: 2661-2671 - [c88]Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le:
MnasNet: Platform-Aware Neural Architecture Search for Mobile. CVPR 2019: 2820-2828 - [c87]Golnaz Ghiasi, Tsung-Yi Lin, Quoc V. Le:
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection. CVPR 2019: 7036-7045 - [c86]Andrew Howard, Ruoming Pang, Hartwig Adam, Quoc V. Le, Mark Sandler, Bo Chen, Weijun Wang, Liang-Chieh Chen, Mingxing Tan, Grace Chu, Vijay Vasudevan, Yukun Zhu:
Searching for MobileNetV3. ICCV 2019: 1314-1324 - [c85]Irwan Bello, Barret Zoph, Quoc Le, Ashish Vaswani, Jonathon Shlens:
Attention Augmented Convolutional Networks. ICCV 2019: 3285-3294 - [c84]Prajit Ramachandran, Quoc V. Le:
Diversity and Depth in Per-Example Routing Models. ICLR (Poster) 2019 - [c83]Daniel S. Park, Jascha Sohl-Dickstein, Quoc V. Le, Samuel L. Smith:
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study. ICML 2019: 5042-5051 - [c82]David R. So, Quoc V. Le, Chen Liang:
The Evolved Transformer. ICML 2019: 5877-5886 - [c81]Mingxing Tan, Quoc V. Le:
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ICML 2019: 6105-6114 - [c80]Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, Quoc V. Le:
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition. INTERSPEECH 2019: 2613-2617 - [c79]Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V. Le, Honglak Lee:
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks. NeurIPS 2019: 81-91 - [c78]Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Xu Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen:
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. NeurIPS 2019: 103-112 - [c77]Gamaleldin F. Elsayed, Simon Kornblith, Quoc V. Le:
Saccader: Improving Accuracy of Hard Attention Models for Vision. NeurIPS 2019: 700-712 - [c76]Brandon Yang, Gabriel Bender, Quoc V. Le, Jiquan Ngiam:
CondConv: Conditionally Parameterized Convolutions for Efficient Inference. NeurIPS 2019: 1305-1316 - [c75]Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, Quoc V. Le:
XLNet: Generalized Autoregressive Pretraining for Language Understanding. NeurIPS 2019: 5754-5764 - [c74]Zhilin Yang, Thang Luong, Ruslan Salakhutdinov, Quoc V. Le:
Mixtape: Breaking the Softmax Bottleneck Efficiently. NeurIPS 2019: 15922-15930 - [i82]Zihang Dai, Zhilin Yang, Yiming Yang, Jaime G. Carbonell, Quoc V. Le, Ruslan Salakhutdinov:
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context. CoRR abs/1901.02860 (2019) - [i81]David R. So, Chen Liang, Quoc V. Le:
The Evolved Transformer. CoRR abs/1901.11117 (2019) - [i80]Brandon Yang, Gabriel Bender, Quoc V. Le, Jiquan Ngiam:
Soft Conditional Computation. CoRR abs/1904.04971 (2019) - [i79]Golnaz Ghiasi, Tsung-Yi Lin, Ruoming Pang, Quoc V. Le:
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection. CoRR abs/1904.07392 (2019) - [i78]Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, Quoc V. Le:
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition. CoRR abs/1904.08779 (2019) - [i77]Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens, Quoc V. Le:
Attention Augmented Convolutional Networks. CoRR abs/1904.09925 (2019) - [i76]Keren Gu, Brandon Yang, Jiquan Ngiam, Quoc V. Le, Jonathon Shlens:
Using Videos to Evaluate Image Model Robustness. CoRR abs/1904.10076 (2019) - [i75]Qizhe Xie, Zihang Dai, Eduard H. Hovy, Minh-Thang Luong, Quoc V. Le:
Unsupervised Data Augmentation. CoRR abs/1904.12848 (2019) - [i74]Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam:
Searching for MobileNetV3. CoRR abs/1905.02244 (2019) - [i73]Daniel S. Park, Jascha Sohl-Dickstein, Quoc V. Le, Samuel L. Smith:
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study. CoRR abs/1905.03776 (2019) - [i72]Mingxing Tan, Quoc V. Le:
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. CoRR abs/1905.11946 (2019) - [i71]Trieu H. Trinh, Minh-Thang Luong, Quoc V. Le:
Selfie: Self-supervised Pretraining for Image Embedding. CoRR abs/1906.02940 (2019) - [i70]Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, Quoc V. Le:
XLNet: Generalized Autoregressive Pretraining for Language Understanding. CoRR abs/1906.08237 (2019) - [i69]Barret Zoph, Ekin D. Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, Quoc V. Le:
Learning Data Augmentation Strategies for Object Detection. CoRR abs/1906.11172 (2019) - [i68]Manas R. Joglekar, Cong Li, Jay K. Adams, Pranav Khaitan, Quoc V. Le:
Neural Input Search for Large Scale Recommendation Models. CoRR abs/1907.04471 (2019) - [i67]Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, Christopher D. Manning, Quoc V. Le:
BAM! Born-Again Multi-Task Networks for Natural Language Understanding. CoRR abs/1907.04829 (2019) - [i66]Mingxing Tan, Quoc V. Le:
MixConv: Mixed Depthwise Convolutional Kernels. CoRR abs/1907.09595 (2019) - [i65]Gamaleldin F. Elsayed, Simon Kornblith, Quoc V. Le:
Saccader: Improving Accuracy of Hard Attention Models for Vision. CoRR abs/1908.07644 (2019) - [i64]Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, Quoc V. Le:
RandAugment: Practical data augmentation with no separate search. CoRR abs/1909.13719 (2019) - [i63]Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V. Le, Honglak Lee:
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks. CoRR abs/1911.01655 (2019) - [i62]Qizhe Xie, Eduard H. Hovy, Minh-Thang Luong, Quoc V. Le:
Self-training with Noisy Student improves ImageNet classification. CoRR abs/1911.04252 (2019) - [i61]Mingxing Tan, Ruoming Pang, Quoc V. Le:
EfficientDet: Scalable and Efficient Object Detection. CoRR abs/1911.09070 (2019) - [i60]Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, Quoc V. Le:
Adversarial Examples Improve Image Recognition. CoRR abs/1911.09665 (2019) - [i59]Bo Chen, Golnaz Ghiasi, Hanxiao Liu, Tsung-Yi Lin, Dmitry Kalenichenko, Hartwig Adam, Quoc V. Le:
MnasFPN: Learning Latency-aware Pyramid Architecture for Object Detection on Mobile Devices. CoRR abs/1912.01106 (2019) - [i58]Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Golnaz Ghiasi, Mingxing Tan, Yin Cui, Quoc V. Le, Xiaodan Song:
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization. CoRR abs/1912.05027 (2019) - [i57]Daniel S. Park, Yu Zhang, Chung-Cheng Chiu, Youzheng Chen, Bo Li, William Chan, Quoc V. Le, Yonghui Wu:
SpecAugment on Large Scale Datasets. CoRR abs/1912.05533 (2019) - 2018
- [j7]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) - [c73]Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le:
Learning Transferable Architectures for Scalable Image Recognition. CVPR 2018: 8697-8710 - [c72]Kevin Clark, Minh-Thang Luong, Christopher D. Manning, Quoc V. Le:
Semi-Supervised Sequence Modeling with Cross-View Training. EMNLP 2018: 1914-1925 - [c71]Wei Wei, Quoc V. Le, Andrew M. Dai, Jia Li:
AirDialogue: An Environment for Goal-Oriented Dialogue Research. EMNLP 2018: 3844-3854 - [c70]David Dohan, David R. So, Quoc V. Le:
Evolving modular neural sequence architectures with genetic programming. GECCO (Companion) 2018: 37-38 - [c69]Ekin Dogus Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le:
Intriguing Properties of Adversarial Examples. ICLR (Workshop) 2018 - [c68]Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V. Le, Jeff Dean:
A Hierarchical Model for Device Placement. ICLR (Poster) 2018 - [c67]Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean:
Faster Discovery of Neural Architectures by Searching for Paths in a Large Model. ICLR (Workshop) 2018 - [c66]Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc V. Le, Jon M. Kleinberg:
Can Deep Reinforcement Learning solve Erdos-Selfridge-Spencer Games? ICLR (Workshop) 2018 - [c65]Prajit Ramachandran, Barret Zoph, Quoc V. Le:
Searching for Activation Functions. ICLR (Workshop) 2018 - [c64]Samuel L. Smith, Pieter-Jan Kindermans, Chris Ying, Quoc V. Le:
Don't Decay the Learning Rate, Increase the Batch Size. ICLR (Poster) 2018 - [c63]Samuel L. Smith, Quoc V. Le:
A Bayesian Perspective on Generalization and Stochastic Gradient Descent. ICLR (Poster) 2018 - [c62]Trieu H. Trinh, Andrew M. Dai, Minh-Thang Luong, Quoc V. Le:
Learning Longer-term Dependencies in RNNs with Auxiliary Losses. ICLR (Workshop) 2018 - [c61]Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le:
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension. ICLR (Poster) 2018 - [c60]Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, Quoc V. Le:
Understanding and Simplifying One-Shot Architecture Search. ICML 2018: 549-558 - [c59]Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean:
Efficient Neural Architecture Search via Parameter Sharing. ICML 2018: 4092-4101 - [c58]Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc V. Le, Jon M. Kleinberg:
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? ICML 2018: 4235-4243 - [c57]Trieu H. Trinh, Andrew M. Dai, Thang Luong, Quoc V. Le:
Learning Longer-term Dependencies in RNNs with Auxiliary Losses. ICML 2018: 4972-4981 - [c56]Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc V. Le, Ni Lao:
Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing. NeurIPS 2018: 10015-10027 - [c55]Golnaz Ghiasi, Tsung-Yi Lin, Quoc V. Le:
DropBlock: A regularization method for convolutional networks. NeurIPS 2018: 10750-10760 - [i56]Daniel A. Abolafia, Mohammad Norouzi, Quoc V. Le:
Neural Program Synthesis with Priority Queue Training. CoRR abs/1801.03526 (2018) - [i55]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 D. Howell, Claire Cui, Greg Corrado, Jeff Dean:
Scalable and accurate deep learning for electronic health records. CoRR abs/1801.07860 (2018) - [i54]Esteban Real, Alok Aggarwal, Yanping Huang, Quoc V. Le:
Regularized Evolution for Image Classifier Architecture Search. CoRR abs/1802.01548 (2018) - [i53]Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean:
Efficient Neural Architecture Search via Parameter Sharing. CoRR abs/1802.03268 (2018) - [i52]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) - [i51]Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, Quoc V. Le:
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension. CoRR abs/1804.09541 (2018) - [i50]Simon Kornblith, Jonathon Shlens, Quoc V. Le:
Do Better ImageNet Models Transfer Better? CoRR abs/1805.08974 (2018) - [i49]Ekin Dogus Cubuk, Barret Zoph, Dandelion Mané, Vijay Vasudevan, Quoc V. Le:
AutoAugment: Learning Augmentation Policies from Data. CoRR abs/1805.09501 (2018) - [i48]Trieu H. Trinh, Quoc V. Le:
A Simple Method for Commonsense Reasoning. CoRR abs/1806.02847 (2018) - [i47]Samuel L. Smith, Daniel Duckworth, Quoc V. Le, Jascha Sohl-Dickstein:
Stochastic natural gradient descent draws posterior samples in function space. CoRR abs/1806.09597 (2018) - [i46]Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc V. Le, Ni Lao:
Memory Augmented Policy Optimization for Program Synthesis with Generalization. CoRR abs/1807.02322 (2018) - [i45]Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Quoc V. Le:
MnasNet: Platform-Aware Neural Architecture Search for Mobile. CoRR abs/1807.11626 (2018) - [i44]Maximilian Alber, Irwan Bello, Barret Zoph, Pieter-Jan Kindermans, Prajit Ramachandran, Quoc V. Le:
Backprop Evolution. CoRR abs/1808.02822 (2018) - [i43]Kevin Clark, Minh-Thang Luong, Christopher D. Manning, Quoc V. Le:
Semi-Supervised Sequence Modeling with Cross-View Training. CoRR abs/1809.08370 (2018) - [i42]Golnaz Ghiasi, Tsung-Yi Lin, Quoc V. Le:
DropBlock: A regularization method for convolutional networks. CoRR abs/1810.12890 (2018) - [i41]Yanping Huang, Yonglong Cheng, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Zhifeng Chen:
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. CoRR abs/1811.06965 (2018) - [i40]Jiquan Ngiam, Daiyi Peng, Vijay Vasudevan, Simon Kornblith, Quoc V. Le, Ruoming Pang:
Domain Adaptive Transfer Learning with Specialist Models. CoRR abs/1811.07056 (2018) - 2017
- [j6]Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda B. Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, Jeffrey Dean:
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation. Trans. Assoc. Comput. Linguistics 5: 339-351 (2017) - [c54]Chen Liang, Jonathan Berant, Quoc V. Le, Kenneth D. Forbus, Ni Lao:
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision. ACL (1) 2017: 23-33 - [c53]Adams Wei Yu, Hongrae Lee, Quoc V. Le:
Learning to Skim Text. ACL (1) 2017: 1880-1890 - [c52]Prajit Ramachandran, Peter J. Liu, Quoc V. Le:
Unsupervised Pretraining for Sequence to Sequence Learning. EMNLP 2017: 383-391 - [c51]Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, Samy Bengio:
Neural Combinatorial Optimization with Reinforcement Learning. ICLR (Workshop) 2017 - [c50]William Chan, Yu Zhang, Quoc V. Le, Navdeep Jaitly:
Latent Sequence Decompositions. ICLR (Poster) 2017 - [c49]David Ha, Andrew M. Dai, Quoc V. Le:
HyperNetworks. ICLR (Poster) 2017 - [c48]Arvind Neelakantan, Quoc V. Le, Martín Abadi, Andrew McCallum, Dario Amodei:
Learning a Natural Language Interface with Neural Programmer. ICLR (Poster) 2017 - [c47]Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, Jeff Dean:
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR (Poster) 2017 - [c46]Barret Zoph, Quoc V. Le:
Neural Architecture Search with Reinforcement Learning. ICLR 2017 - [c45]Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc V. Le:
Neural Optimizer Search with Reinforcement Learning. ICML 2017: 459-468 - [c44]Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean:
Device Placement Optimization with Reinforcement Learning. ICML 2017: 2430-2439 - [c43]Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka I. Leon-Suematsu, Jie Tan, Quoc V. Le, Alexey Kurakin:
Large-Scale Evolution of Image Classifiers. ICML 2017: 2902-2911 - [c42]Yuxuan Wang, R. J. Skerry-Ryan, Daisy Stanton, Yonghui Wu, Ron J. Weiss, Navdeep Jaitly, Zongheng Yang, Ying Xiao, Zhifeng Chen, Samy Bengio, Quoc V. Le, Yannis Agiomyrgiannakis, Rob Clark, Rif A. Saurous:
Tacotron: Towards End-to-End Speech Synthesis. INTERSPEECH 2017: 4006-4010 - [c41]Denny Britz, Quoc V. Le, Reid Pryzant:
Effective Domain Mixing for Neural Machine Translation. WMT 2017: 118-126 - [i39]Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc V. Le, Geoffrey E. Hinton, Jeff Dean:
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. CoRR abs/1701.06538 (2017) - [i38]Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka I. Leon-Suematsu, Quoc V. Le, Alex Kurakin:
Large-Scale Evolution of Image Classifiers. CoRR abs/1703.01041 (2017) - [i37]Denny Britz, Anna Goldie, Minh-Thang Luong, Quoc V. Le:
Massive Exploration of Neural Machine Translation Architectures. CoRR abs/1703.03906 (2017) - [i36]Yuxuan Wang, R. J. Skerry-Ryan, Daisy Stanton, Yonghui Wu, Ron J. Weiss, Navdeep Jaitly, Zongheng Yang, Ying Xiao, Zhifeng Chen, Samy Bengio, Quoc V. Le, Yannis Agiomyrgiannakis, Rob Clark, Rif A. Saurous:
Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model. CoRR abs/1703.10135 (2017) - [i35]Adams Wei Yu, Hongrae Lee, Quoc V. Le:
Learning to Skim Text. CoRR abs/1704.06877 (2017) - [i34]Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean:
Device Placement Optimization with Reinforcement Learning. CoRR abs/1706.04972 (2017) - [i33]Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le:
Learning Transferable Architectures for Scalable Image Recognition. CoRR abs/1707.07012 (2017) - [i32]Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc V. Le:
Neural Optimizer Search with Reinforcement Learning. CoRR abs/1709.07417 (2017) - [i31]Prajit Ramachandran, Barret Zoph, Quoc V. Le:
Searching for Activation Functions. CoRR abs/1710.05941 (2017) - [i30]Samuel L. Smith, Quoc V. Le:
A Bayesian Perspective on Generalization and Stochastic Gradient Descent. CoRR abs/1710.06451 (2017) - [i29]Samuel L. Smith, Pieter-Jan Kindermans, Quoc V. Le:
Don't Decay the Learning Rate, Increase the Batch Size. CoRR abs/1711.00489 (2017) - [i28]Maithra Raghu, Alex Irpan, Jacob Andreas, Robert Kleinberg, Quoc V. Le, Jon M. Kleinberg:
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? CoRR abs/1711.02301 (2017) - [i27]Ekin Dogus Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le:
Intriguing Properties of Adversarial Examples. CoRR abs/1711.02846 (2017) - 2016
- [c40]William Chan, Navdeep Jaitly, Quoc V. Le, Oriol Vinyals:
Listen, attend and spell: A neural network for large vocabulary conversational speech recognition. ICASSP 2016: 4960-4964 - [c39]Navdeep Jaitly, Quoc V. Le, Oriol Vinyals, Ilya Sutskever, David Sussillo, Samy Bengio:
An Online Sequence-to-Sequence Model Using Partial Conditioning. NIPS 2016: 5067-5075 - [c38]Quoc V. Le:
End-to-end Learning for Text and Speech. SSW 2016 - [c37]Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, Lukasz Kaiser:
Multi-task Sequence to Sequence Learning. ICLR (Poster) 2016 - [c36]Arvind Neelakantan, Quoc V. Le, Ilya Sutskever:
Neural Programmer: Inducing Latent Programs with Gradient Descent. ICLR (Poster) 2016 - [i26]Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean:
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. CoRR abs/1609.08144 (2016) - [i25]David Ha, Andrew M. Dai, Quoc V. Le:
HyperNetworks. CoRR abs/1609.09106 (2016) - [i24]William Chan, Yu Zhang, Quoc V. Le, Navdeep Jaitly:
Latent Sequence Decompositions. CoRR abs/1610.03035 (2016) - [i23]Chen Liang, Jonathan Berant, Quoc V. Le, Kenneth D. Forbus, Ni Lao:
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision. CoRR abs/1611.00020 (2016) - [i22]Barret Zoph, Quoc V. Le:
Neural Architecture Search with Reinforcement Learning. CoRR abs/1611.01578 (2016) - [i21]Prajit Ramachandran, Peter J. Liu, Quoc V. Le:
Unsupervised Pretraining for Sequence to Sequence Learning. CoRR abs/1611.02683 (2016) - [i20]Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda B. Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, Jeffrey Dean:
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation. CoRR abs/1611.04558 (2016) - [i19]Arvind Neelakantan, Quoc V. Le, Martín Abadi, Andrew McCallum, Dario Amodei:
Learning a Natural Language Interface with Neural Programmer. CoRR abs/1611.08945 (2016) - [i18]Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, Samy Bengio:
Neural Combinatorial Optimization with Reinforcement Learning. CoRR abs/1611.09940 (2016) - [i17]Chen Liang, Jonathan Berant, Quoc V. Le, Kenneth D. Forbus, Ni Lao:
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version). CoRR abs/1612.01197 (2016) - 2015
- [c35]Thang Luong, Ilya Sutskever, Quoc V. Le, Oriol Vinyals, Wojciech Zaremba:
Addressing the Rare Word Problem in Neural Machine Translation. ACL (1) 2015: 11-19 - [c34]Andrew M. Dai, Quoc V. Le:
Semi-supervised Sequence Learning. NIPS 2015: 3079-3087 - [i16]Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton:
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units. CoRR abs/1504.00941 (2015) - [i15]Oriol Vinyals, Quoc V. Le:
A Neural Conversational Model. CoRR abs/1506.05869 (2015) - [i14]Andrew M. Dai, Christopher Olah, Quoc V. Le:
Document Embedding with Paragraph Vectors. CoRR abs/1507.07998 (2015) - [i13]William Chan, Navdeep Jaitly, Quoc V. Le, Oriol Vinyals:
Listen, Attend and Spell. CoRR abs/1508.01211 (2015) - [i12]Andrew M. Dai, Quoc V. Le:
Semi-supervised Sequence Learning. CoRR abs/1511.01432 (2015) - [i11]Navdeep Jaitly, Quoc V. Le, Oriol Vinyals, Ilya Sutskever, Samy Bengio:
An Online Sequence-to-Sequence Model Using Partial Conditioning. CoRR abs/1511.04868 (2015) - [i10]Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, James Martens:
Adding Gradient Noise Improves Learning for Very Deep Networks. CoRR abs/1511.06807 (2015) - 2014
- [j5]Richard Socher, Andrej Karpathy, Quoc V. Le, Christopher D. Manning, Andrew Y. Ng:
Grounded Compositional Semantics for Finding and Describing Images with Sentences. Trans. Assoc. Comput. Linguistics 2: 207-218 (2014) - [c33]Quoc V. Le, Tomás Mikolov:
Distributed Representations of Sentences and Documents. ICML 2014: 1188-1196 - [c32]Ilya Sutskever, Oriol Vinyals, Quoc V. Le:
Sequence to Sequence Learning with Neural Networks. NIPS 2014: 3104-3112 - [i9]Quoc V. Le, Tomás Mikolov:
Distributed Representations of Sentences and Documents. CoRR abs/1405.4053 (2014) - [i8]Quoc Viet Le, Tamás Sarlós, Alexander Johannes Smola:
Fastfood: Approximate Kernel Expansions in Loglinear Time. CoRR abs/1408.3060 (2014) - [i7]Ilya Sutskever, Oriol Vinyals, Quoc V. Le:
Sequence to Sequence Learning with Neural Networks. CoRR abs/1409.3215 (2014) - [i6]Thang Luong, Ilya Sutskever, Quoc V. Le, Oriol Vinyals, Wojciech Zaremba:
Addressing the Rare Word Problem in Neural Machine Translation. CoRR abs/1410.8206 (2014) - 2013
- [b1]Quoc V. Le:
Scalable feature learning. Stanford University, USA, 2013 - [c31]Matthew D. Zeiler, Marc'Aurelio Ranzato, Rajat Monga, Mark Z. Mao, K. Yang, Quoc Viet Le, Patrick Nguyen, Andrew W. Senior, Vincent Vanhoucke, Jeffrey Dean, Geoffrey E. Hinton:
On rectified linear units for speech processing. ICASSP 2013: 3517-3521 - [c30]Quoc V. Le:
Building high-level features using large scale unsupervised learning. ICASSP 2013: 8595-8598 - [c29]Quoc V. Le, Tamás Sarlós, Alexander J. Smola:
Fastfood - Computing Hilbert Space Expansions in loglinear time. ICML (3) 2013: 244-252 - [i5]Tomás Mikolov, Quoc V. Le, Ilya Sutskever:
Exploiting Similarities among Languages for Machine Translation. CoRR abs/1309.4168 (2013) - [i4]Samy Bengio, Jeffrey Dean, Dumitru Erhan, Eugene Ie, Quoc V. Le, Andrew Rabinovich, Jonathon Shlens, Yoram Singer:
Using Web Co-occurrence Statistics for Improving Image Categorization. CoRR abs/1312.5697 (2013) - 2012
- [c28]Quoc V. Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Greg Corrado, Kai Chen, Jeffrey Dean, Andrew Y. Ng:
Building high-level features using large scale unsupervised learning. ICML 2012 - [c27]Andrew L. Maas, Quoc V. Le, Tyler M. O'Neil, Oriol Vinyals, Patrick Nguyen, Andrew Y. Ng:
Recurrent Neural Networks for Noise Reduction in Robust ASR. INTERSPEECH 2012: 22-25 - [c26]Quoc V. Le, Ju Han, Joe W. Gray, Paul T. Spellman, Alexander Borowsky, Bahram Parvin:
Learning invariant features of tumor signatures. ISBI 2012: 302-305 - [c25]Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc'Aurelio Ranzato, Andrew W. Senior, Paul A. Tucker, Ke Yang, Andrew Y. Ng:
Large Scale Distributed Deep Networks. NIPS 2012: 1232-1240 - 2011
- [c24]Quoc V. Le, Will Y. Zou, Serena Y. Yeung, Andrew Y. Ng:
Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. CVPR 2011: 3361-3368 - [c23]Quoc V. Le, Jiquan Ngiam, Adam Coates, Ahbik Lahiri, Bobby Prochnow, Andrew Y. Ng:
On optimization methods for deep learning. ICML 2011: 265-272 - [c22]Quoc V. Le, Alexandre Karpenko, Jiquan Ngiam, Andrew Y. Ng:
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning. NIPS 2011: 1017-1025 - [i3]Quoc V. Le, Rajat Monga, Matthieu Devin, Greg Corrado, Kai Chen, Marc'Aurelio Ranzato, Jeffrey Dean, Andrew Y. Ng:
Building high-level features using large scale unsupervised learning. CoRR abs/1112.6209 (2011) - 2010
- [j4]Choon Hui Teo, S. V. N. Vishwanathan, Alexander J. Smola, Quoc V. Le:
Bundle Methods for Regularized Risk Minimization. J. Mach. Learn. Res. 11: 311-365 (2010) - [c21]Quoc V. Le, David Kamm, Arda F. Kara, Andrew Y. Ng:
Learning to grasp objects with multiple contact points. ICRA 2010: 5062-5069 - [c20]Deepak Rao, Quoc V. Le, Thanathorn Phoka, Morgan Quigley, Attawith Sudsang, Andrew Y. Ng:
Grasping novel objects with depth segmentation. IROS 2010: 2578-2585 - [c19]Morgan Quigley, Reuben D. Brewer, Sai Prashanth Soundararaj, Vijay Pradeep, Quoc V. Le, Andrew Y. Ng:
Low-cost accelerometers for robotic manipulator perception. IROS 2010: 6168-6174 - [c18]Quoc V. Le, Jiquan Ngiam, Zhenghao Chen, Daniel Jin hao Chia, Pang Wei Koh, Andrew Y. Ng:
Tiled convolutional neural networks. NIPS 2010: 1279-1287
2000 – 2009
- 2009
- [j3]Novi Quadrianto, Alexander J. Smola, Tibério S. Caetano, Quoc V. Le:
Estimating Labels from Label Proportions. J. Mach. Learn. Res. 10: 2349-2374 (2009) - [j2]Tibério S. Caetano, Julian J. McAuley, Li Cheng, Quoc V. Le, Alexander J. Smola:
Learning Graph Matching. IEEE Trans. Pattern Anal. Mach. Intell. 31(6): 1048-1058 (2009) - [c17]Chuong B. Do, Quoc V. Le, Chuan-Sheng Foo:
Proximal regularization for online and batch learning. ICML 2009: 257-264 - [c16]Morgan Quigley, Siddharth Batra, Stephen Gould, Ellen Klingbeil, Quoc V. Le, Ashley Wellman, Andrew Y. Ng:
High-accuracy 3D sensing for mobile manipulation: Improving object detection and door opening. ICRA 2009: 2816-2822 - [c15]Quoc V. Le, Andrew Y. Ng:
Joint calibration of multiple sensors. IROS 2009: 3651-3658 - [c14]Adam Coates, Paul Baumstarck, Quoc V. Le, Andrew Y. Ng:
Scalable learning for object detection with GPU hardware. IROS 2009: 4287-4293 - [c13]Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee, Andrew Y. Ng:
Measuring Invariances in Deep Networks. NIPS 2009: 646-654 - 2008
- [c12]Novi Quadrianto, Alexander J. Smola, Tibério S. Caetano, Quoc V. Le:
Estimating labels from label proportions. ICML 2008: 776-783 - [c11]Olivier Chapelle, Chuong B. Do, Quoc V. Le, Alexander J. Smola, Choon Hui Teo:
Tighter Bounds for Structured Estimation. NIPS 2008: 281-288 - [i2]Tibério S. Caetano, Julian J. McAuley, Li Cheng, Quoc V. Le, Alexander J. Smola:
Learning Graph Matching. CoRR abs/0806.2890 (2008) - 2007
- [c10]Tibério S. Caetano, Li Cheng, Quoc V. Le, Alexander J. Smola:
Learning Graph Matching. ICCV 2007: 1-8 - [c9]Choon Hui Teo, Alexander J. Smola, S. V. N. Vishwanathan, Quoc V. Le:
A scalable modular convex solver for regularized risk minimization. KDD 2007: 727-736 - [c8]Alexander J. Smola, S. V. N. Vishwanathan, Quoc V. Le:
Bundle Methods for Machine Learning. NIPS 2007: 1377-1384 - [c7]Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alexander J. Smola:
COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking . NIPS 2007: 1593-1600 - [i1]Quoc V. Le, Alexander J. Smola:
Direct Optimization of Ranking Measures. CoRR abs/0704.3359 (2007) - 2006
- [j1]Ichiro Takeuchi, Quoc V. Le, Tim D. Sears, Alexander J. Smola:
Nonparametric Quantile Estimation. J. Mach. Learn. Res. 7: 1231-1264 (2006) - [c6]Quoc V. Le, Alexander J. Smola, Thomas Gärtner, Yasemin Altun:
Transductive Gaussian Process Regression with Automatic Model Selection. ECML 2006: 306-317 - [c5]Quoc V. Le, Alexander J. Smola, Thomas Gärtner:
Simpler knowledge-based support vector machines. ICML 2006: 521-528 - [c4]Christopher J. C. Burges, Robert Ragno, Quoc Viet Le:
Learning to Rank with Nonsmooth Cost Functions. NIPS 2006: 193-200 - 2005
- [c3]Quoc V. Le, Alexander J. Smola, Stéphane Canu:
Heteroscedastic Gaussian process regression. ICML 2005: 489-496 - [c2]Thomas Gärtner, Quoc V. Le, Simon Burton, Alexander J. Smola, S. V. N. Vishwanathan:
Large-Scale Multiclass Transduction. NIPS 2005: 411-418 - [c1]Robert McCann, Bedoor K. AlShebli, Quoc Le, Hoa Nguyen, Long H. Vu, AnHai Doan:
Mapping Maintenance for Data Integration Systems. VLDB 2005: 1018-1030
Coauthor Index
Ekin Dogus Cubuk
aka: Ekin D. Cubuk
aka: Ekin D. Cubuk
Jeffrey Dean
aka: Jeff Dean
aka: Jeff Dean
Alexander J. Smola
aka: Alexander Johannes Smola
aka: Alexander Johannes Smola
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