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2020 – today
- 2024
[j3]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)- [c23]Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Kumar Dubey, Alexandre Ramé, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Léonard Hussenot, Olivier Bachem, Edouard Leurent:
Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning. EMNLP (Findings) 2024: 2153-2186 - [c22]Yunxuan Li, Yibing Du, Jiageng Zhang, Le Hou, Peter Grabowski, Yeqing Li, Eugene Ie:
Improving Multi-Agent Debate with Sparse Communication Topology. EMNLP (Findings) 2024: 7281-7294 - [c21]Zihan Wang, Yunxuan Li, Yuexin Wu, Liangchen Luo, Le Hou, Hongkun Yu, Jingbo Shang:
Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision. EMNLP (Findings) 2024: 7309-7319 - [c20]Ziqi Wang, Le Hou, Tianjian Lu, Yuexin Wu, Yunxuan Li, Hongkun Yu, Heng Ji:
Enabling Lanuguage Models to Implicitly Learn Self-Improvement. ICLR 2024 - [c19]Sheng Shen, Le Hou, Yanqi Zhou, Nan Du, Shayne Longpre, Jason Wei, Hyung Won Chung, Barret Zoph, William Fedus, Xinyun Chen, Tu Vu, Yuexin Wu, Wuyang Chen, Albert Webson, Yunxuan Li, Vincent Y. Zhao, Hongkun Yu, Kurt Keutzer, Trevor Darrell, Denny Zhou:
Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for Large Language Models. ICLR 2024 - [c18]Chiyu Zhang, Honglong Cai, Yuezhang Li, Yuexin Wu, Le Hou, Muhammad Abdul-Mageed:
Distilling Text Style Transfer With Self-Explanation From LLMs. NAACL (Student Research Workshop) 2024: 200-211 - [i33]Tao Tu, Anil Palepu, Mike Schaekermann, Khaled Saab, Jan Freyberg, Ryutaro Tanno, Amy Wang, Brenna Li, Mohamed Amin, Nenad Tomasev, Shekoofeh Azizi, Karan Singhal, Yong Cheng, Le Hou, Albert Webson, Kavita Kulkarni, S. Sara Mahdavi, Christopher Semturs, Juraj Gottweis, Joelle K. Barral, Katherine Chou, Gregory S. Corrado, Yossi Matias, Alan Karthikesalingam, Vivek Natarajan:
Towards Conversational Diagnostic AI. CoRR abs/2401.05654 (2024) - [i32]Zihan Wang, Yunxuan Li, Yuexin Wu, Liangchen Luo, Le Hou, Hongkun Yu, Jingbo Shang:
Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision. CoRR abs/2402.02658 (2024) - [i31]Chiyu Zhang, Honglong Cai, Yuezhang Li, Yuexin Wu, Le Hou, Muhammad Abdul-Mageed:
Distilling Text Style Transfer With Self-Explanation From LLMs. CoRR abs/2403.01106 (2024) - [i30]Khaled Saab, Tao Tu, Wei-Hung Weng, Ryutaro Tanno, David Stutz, Ellery Wulczyn, Fan Zhang, Tim Strother, Chunjong Park, Elahe Vedadi, Juanma Zambrano Chaves, Szu-Yeu Hu, Mike Schaekermann, Aishwarya Kamath, Yong Cheng, David G. T. Barrett, Cathy Cheung, Basil Mustafa, Anil Palepu, Daniel McDuff, Le Hou, Tomer Golany, Luyang Liu, Jean-Baptiste Alayrac, Neil Houlsby, Nenad Tomasev, Jan Freyberg, Charles Lau, Jonas Kemp, Jeremy Lai, Shekoofeh Azizi, Kimberly Kanada, SiWai Man, Kavita Kulkarni, Ruoxi Sun, Siamak Shakeri, Luheng He, Benjamin Caine, Albert Webson, Natasha Latysheva, Melvin Johnson, Philip Andrew Mansfield, Jian Lu, Ehud Rivlin, Jesper Anderson, Bradley Green, Renee Wong, Jonathan Krause, Jonathon Shlens, Ewa Dominowska, S. M. Ali Eslami, Katherine Chou, Claire Cui, Oriol Vinyals, Koray Kavukcuoglu, James Manyika, Jeff Dean, Demis Hassabis, Yossi Matias, Dale R. Webster, Joelle K. Barral, Greg Corrado, Christopher Semturs, S. Sara Mahdavi, Juraj Gottweis, Alan Karthikesalingam, Vivek Natarajan:
Capabilities of Gemini Models in Medicine. CoRR abs/2404.18416 (2024) - [i29]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) - [i28]Yunxuan Li, Yibing Du, Jiageng Zhang, Le Hou, Peter Grabowski, Yeqing Li, Eugene Ie:
Improving Multi-Agent Debate with Sparse Communication Topology. CoRR abs/2406.11776 (2024) - [i27]Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Avinava Dubey, Alexandre Ramé, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Léonard Hussenot, Olivier Bachem, Edouard Leurent:
Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning. CoRR abs/2407.15762 (2024) - 2023
- [c17]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 - [c16]Jiaxin Huang, Shixiang Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, Jiawei Han:
Large Language Models Can Self-Improve. EMNLP 2023: 1051-1068 - [c15]Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji:
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation. ICLR 2023 - [c14]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 - [c13]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 - [c12]Vu Nguyen, Prantik Howlader, Le Hou, Dimitris Samaras, Rajarsi R. Gupta, Joel H. Saltz:
Few Shot Hematopoietic Cell Classification. MIDL 2023: 1085-1103 - [i26]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) - [i25]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) - [i24]Karan Singhal, Tao Tu, Juraj Gottweis, Rory Sayres, Ellery Wulczyn, Le Hou, Kevin Clark, Stephen Pfohl, Heather Cole-Lewis, Darlene Neal, Mike Schaekermann, Amy Wang, Mohamed Amin, Sami Lachgar, Philip Andrew Mansfield, Sushant Prakash, Bradley Green, Ewa Dominowska, Blaise Agüera y Arcas, Nenad Tomasev, Yun Liu
, Renee Wong, Christopher Semturs, S. Sara Mahdavi, Joelle K. Barral, Dale R. Webster, Gregory S. Corrado, Yossi Matias, Shekoofeh Azizi, Alan Karthikesalingam, Vivek Natarajan:
Towards Expert-Level Medical Question Answering with Large Language Models. CoRR abs/2305.09617 (2023) - [i23]Sheng Shen, Le Hou, Yanqi Zhou, Nan Du, Shayne Longpre, Jason Wei, Hyung Won Chung, Barret Zoph, William Fedus, Xinyun Chen, Tu Vu, Yuexin Wu, Wuyang Chen, Albert Webson, Yunxuan Li, Vincent Y. Zhao, Hongkun Yu, Kurt Keutzer, Trevor Darrell, Denny Zhou:
Flan-MoE: Scaling Instruction-Finetuned Language Models with Sparse Mixture of Experts. CoRR abs/2305.14705 (2023) - [i22]Ziqi Wang, Le Hou, Tianjian Lu, Yuexin Wu, Yunxuan Li, Hongkun Yu, Heng Ji:
Enable Language Models to Implicitly Learn Self-Improvement From Data. CoRR abs/2310.00898 (2023) - [i21]Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, Le Hou:
Instruction-Following Evaluation for Large Language Models. CoRR abs/2311.07911 (2023) - [i20]Daniel McDuff, Mike Schaekermann, Tao Tu, Anil Palepu, Amy Wang, Jake Garrison, Karan Singhal, Yash Sharma, Shekoofeh Azizi, Kavita Kulkarni, Le Hou, Yong Cheng, Yun Liu, S. Sara Mahdavi, Sushant Prakash, Anupam Pathak, Christopher Semturs, Shwetak N. Patel, Dale R. Webster, Ewa Dominowska, Juraj Gottweis, Joelle K. Barral, Katherine Chou, Gregory S. Corrado, Yossi Matias, Jake Sunshine, Alan Karthikesalingam, Vivek Natarajan:
Towards Accurate Differential Diagnosis with Large Language Models. CoRR abs/2312.00164 (2023) - 2022
- [c11]Le Hou, Richard Yuanzhe Pang, Tianyi Zhou, Yuexin Wu, Xinying Song, Xiaodan Song, Denny Zhou:
Token Dropping for Efficient BERT Pretraining. ACL (1) 2022: 3774-3784 - [i19]Le Hou, Richard Yuanzhe Pang, Tianyi Zhou, Yuexin Wu, Xinying Song, Xiaodan Song, Denny Zhou:
Token Dropping for Efficient BERT Pretraining. CoRR abs/2203.13240 (2022) - [i18]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) - [i17]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) - [i16]Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, Jiawei Han:
Large Language Models Can Self-Improve. CoRR abs/2210.11610 (2022) - [i15]Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji:
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation. CoRR abs/2210.11768 (2022) - 2021
- [j2]Le Hou, Tomás F. Yago Vicente, Minh Hoai, Dimitris Samaras:
Large Scale Shadow Annotation and Detection Using Lazy Annotation and Stacked CNNs. IEEE Trans. Pattern Anal. Mach. Intell. 43(4): 1337-1351 (2021) - [i14]Shuo Yang, Le Hou, Xiaodan Song, Qiang Liu, Denny Zhou:
Speeding up Deep Model Training by Sharing Weights and Then Unsharing. CoRR abs/2110.03848 (2021) - 2020
- [c10]Shahira Abousamra, Danielle Fassler, Le Hou, Yuwei Zhang, Rajarsi Gupta, Tahsin M. Kurç, Luisa F. Escobar-Hoyos, Dimitris Samaras, Beatrice Knudson, Kenneth Shroyer, Joel H. Saltz, Chao Chen:
Weakly-Supervised Deep Stain Decomposition for Multiplex IHC Images. ISBI 2020: 481-485 - [i13]Le Hou, Rajarsi Gupta, John S. Van Arnam, Yuwei Zhang, Kaustubh Sivalenka, Dimitris Samaras, Tahsin M. Kurç, Joel H. Saltz:
Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types. CoRR abs/2002.07913 (2020) - [i12]Noam Shazeer, Zhenzhong Lan, Youlong Cheng, Nan Ding, Le Hou:
Talking-Heads Attention. CoRR abs/2003.02436 (2020)
2010 – 2019
- 2019
- [j1]Le Hou, Vu Nguyen, Ariel B. Kanevsky, Dimitris Samaras, Tahsin M. Kurç, Tianhao Zhao, Rajarsi R. Gupta, Yi Gao, Wenjin Chen, David J. Foran, Joel H. Saltz:
Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images. Pattern Recognit. 86: 188-200 (2019) - [c9]Robert M. Patton, Shahira Abousamra, Dimitris Samaras, Joel H. Saltz, J. Travis Johnston, Steven R. Young, Catherine D. Schuman, Thomas E. Potok, Derek C. Rose, Seung-Hwan Lim, Junghoon Chae, Le Hou:
Exascale Deep Learning to Accelerate Cancer Research. IEEE BigData 2019: 1488-1496 - [c8]Tahsin M. Kurç, Ashish Sharma, Rajarsi Gupta, Le Hou, Han Le, Shahira Abousamra, Erich Bremer, Ryan Birmingham, Tammy Diprima, Nan Li, Feiqiao Wang, Joseph Balsamo, Whitney Bremer, Dimitris Samaras, Joel H. Saltz:
From Whole Slide Tissues to Knowledge: Mapping Sub-cellular Morphology of Cancer. BrainLes@MICCAI (2) 2019: 371-379 - [c7]Le Hou, Ayush Agarwal, Dimitris Samaras, Tahsin M. Kurç, Rajarsi R. Gupta, Joel H. Saltz:
Robust Histopathology Image Analysis: To Label or to Synthesize? CVPR 2019: 8533-8542 - [c6]Caleb Robinson, Le Hou, Kolya Malkin, Rachel Soobitsky, Jacob Czawlytko, Bistra Dilkina, Nebojsa Jojic:
Large Scale High-Resolution Land Cover Mapping With Multi-Resolution Data. CVPR 2019: 12726-12735 - [c5]Kolya Malkin, Caleb Robinson, Le Hou, Rachel Soobitsky, Jacob Czawlytko, Dimitris Samaras, Joel H. Saltz, Lucas Joppa, Nebojsa Jojic:
Label super-resolution networks. ICLR (Poster) 2019 - [i11]Maozheng Zhao, Le Hou, Han Le, Dimitris Samaras, Nebojsa Jojic, Danielle Fassler, Tahsin M. Kurç, Rajarsi R. Gupta, Kolya Malkin, Shahira Abousamra, Kenneth Shroyer, Joel H. Saltz:
Label Super Resolution with Inter-Instance Loss. CoRR abs/1904.04429 (2019) - [i10]Han Le, Rajarsi R. Gupta, Le Hou, Shahira Abousamra, Danielle Fassler, Tahsin M. Kurç, Dimitris Samaras, Rebecca Batiste, Tianhao Zhao, Alison L. Van Dyke, Ashish Sharma, Erich Bremer, Jonas S. Almeida, Joel H. Saltz:
Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer. CoRR abs/1905.10841 (2019) - [i9]Shahira Abousamra, Le Hou, Rajarsi R. Gupta, Chao Chen, Dimitris Samaras, Tahsin M. Kurç, Rebecca Batiste, Tianhao Zhao, Kenneth Shroyer, Joel H. Saltz:
Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types. CoRR abs/1907.03960 (2019) - [i8]Le Hou, Youlong Cheng, Noam Shazeer, Niki Parmar, Yeqing Li, Panagiotis Korfiatis, Travis M. Drucker, Daniel J. Blezek, Xiaodan Song:
High Resolution Medical Image Analysis with Spatial Partitioning. CoRR abs/1909.03108 (2019) - [i7]Robert M. Patton, J. Travis Johnston, Steven R. Young, Catherine D. Schuman, Thomas E. Potok, Derek C. Rose, Seung-Hwan Lim, Junghoon Chae, Le Hou, Shahira Abousamra, Dimitris Samaras, Joel H. Saltz:
Exascale Deep Learning to Accelerate Cancer Research. CoRR abs/1909.12291 (2019) - 2017
- [c4]Le Hou, Dimitris Samaras, Tahsin M. Kurç, Yi Gao, Joel H. Saltz:
ConvNets with Smooth Adaptive Activation Functions for Regression. AISTATS 2017: 430-439 - [c3]Veda Murthy, Le Hou, Dimitris Samaras, Tahsin M. Kurç, Joel H. Saltz:
Center-Focusing Multi-task CNN with Injected Features for Classification of Glioma Nuclear Images. WACV 2017: 834-841 - [i6]Le Hou, Vu Nguyen, Dimitris Samaras, Tahsin M. Kurç, Yi Gao, Tianhao Zhao, Joel H. Saltz:
Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images. CoRR abs/1704.00406 (2017) - [i5]Le Hou, Ayush Agarwal, Dimitris Samaras, Tahsin M. Kurç, Rajarsi R. Gupta, Joel H. Saltz:
Unsupervised Histopathology Image Synthesis. CoRR abs/1712.05021 (2017) - 2016
- [c2]Le Hou, Dimitris Samaras, Tahsin M. Kurç, Yi Gao, James E. Davis, Joel H. Saltz:
Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification. CVPR 2016: 2424-2433 - [c1]Tomás F. Yago Vicente, Le Hou, Chen-Ping Yu, Minh Hoai, Dimitris Samaras:
Large-Scale Training of Shadow Detectors with Noisily-Annotated Shadow Examples. ECCV (6) 2016: 816-832 - [i4]Le Hou, Dimitris Samaras, Tahsin M. Kurç, Yi Gao, Joel H. Saltz:
Neural Networks with Smooth Adaptive Activation Functions for Regression. CoRR abs/1608.06557 (2016) - [i3]Le Hou, Chen-Ping Yu, Dimitris Samaras:
Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks. CoRR abs/1611.05916 (2016) - [i2]Veda Murthy, Le Hou, Dimitris Samaras, Tahsin M. Kurç, Joel H. Saltz:
Center-Focusing Multi-task CNN with Injected Features for Classification of Glioma Nuclear Images. CoRR abs/1612.06825 (2016) - 2015
- [i1]Le Hou, Dimitris Samaras, Tahsin M. Kurç, Yi Gao, James E. Davis, Joel H. Saltz:
Efficient Multiple Instance Convolutional Neural Networks for Gigapixel Resolution Image Classification. CoRR abs/1504.07947 (2015)
Coauthor Index
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