Le Hou

According to our database, Le Hou authored at least 41 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Distilling Text Style Transfer With Self-Explanation From LLMs.

Chiyu Zhang, Honglong Cai, Yuezhang Li, Yuexin Wu, Le Hou, Muhammad Abdul-Mageed

CoRR, 2024

Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision.

Zihan Wang, Yunxuan Li, Yuexin Wu, Liangchen Luo, Le Hou, Hongkun Yu, Jingbo Shang

CoRR, 2024

Towards Conversational Diagnostic AI.

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

CoRR, 2024

2023

Towards Accurate Differential Diagnosis with Large Language Models.

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

CoRR, 2023

Instruction-Following Evaluation for Large Language Models.

Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, Le Hou

CoRR, 2023

Enable Language Models to Implicitly Learn Self-Improvement From Data.

Ziqi Wang, Le Hou, Tianjian Lu, Yuexin Wu, Yunxuan Li, Hongkun Yu, Heng Ji

CoRR, 2023

Flan-MoE: Scaling Instruction-Finetuned Language Models with Sparse Mixture of Experts.

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

CoRR, 2023

Towards Expert-Level Medical Question Answering with Large Language Models.

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

CoRR, 2023

Few Shot Hematopoietic Cell Classification.

Vu Nguyen, Prantik Howlader, Le Hou, Dimitris Samaras, Rajarsi R. Gupta, Joel H. Saltz

Proceedings of the Medical Imaging with Deep Learning, 2023

The Flan Collection: Designing Data and Methods for Effective Instruction Tuning.

Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V. Le, Barret Zoph, Jason Wei, Adam Roberts

Proceedings of the International Conference on Machine Learning, 2023

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, Ed H. Chi

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation.

Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Symbol tuning improves in-context learning in language models.

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

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Large Language Models Can Self-Improve.

Jiaxin Huang, Shixiang Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, Jiawei Han

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

2022

Scaling Instruction-Finetuned Language Models.

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

CoRR, 2022

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Olivier Bousquet, Quoc Le, Ed H. Chi

CoRR, 2022

Token Dropping for Efficient BERT Pretraining.

Le Hou, Richard Yuanzhe Pang, Tianyi Zhou, Yuexin Wu, Xinying Song, Xiaodan Song, Denny Zhou

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021

Large Scale Shadow Annotation and Detection Using Lazy Annotation and Stacked CNNs.

Le Hou, Tomás F. Yago Vicente, Minh Hoai, Dimitris Samaras

IEEE Trans. Pattern Anal. Mach. Intell., 2021

Speeding up Deep Model Training by Sharing Weights and Then Unsharing.

Shuo Yang, Le Hou, Xiaodan Song, Qiang Liu, Denny Zhou

CoRR, 2021

2020

Talking-Heads Attention.

Noam Shazeer, Zhenzhong Lan, Youlong Cheng, Nan Ding, Le Hou

CoRR, 2020

Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types.

Le Hou, Rajarsi Gupta, John S. Van Arnam, Yuwei Zhang, Kaustubh Sivalenka, Dimitris Samaras, Tahsin M. Kurç, Joel H. Saltz

CoRR, 2020

Weakly-Supervised Deep Stain Decomposition for Multiplex IHC Images.

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

Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020

2019

Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images.

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

Pattern Recognit., 2019

High Resolution Medical Image Analysis with Spatial Partitioning.

Le Hou, Youlong Cheng, Noam Shazeer, Niki Parmar, Yeqing Li, Panagiotis Korfiatis, Travis M. Drucker, Daniel J. Blezek, Xiaodan Song

CoRR, 2019

Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types.

Shahira Abousamra, Le Hou, Rajarsi R. Gupta, Chao Chen, Dimitris Samaras, Tahsin M. Kurç, Rebecca Batiste, Tianhao Zhao, Kenneth Shroyer, Joel H. Saltz

CoRR, 2019

Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer.

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

CoRR, 2019

Label Super Resolution with Inter-Instance Loss.

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

CoRR, 2019

Label super-resolution networks.

Kolya Malkin, Caleb Robinson, Le Hou, Rachel Soobitsky, Jacob Czawlytko, Dimitris Samaras, Joel H. Saltz, Lucas Joppa, Nebojsa Jojic

Proceedings of the 7th International Conference on Learning Representations, 2019

Large Scale High-Resolution Land Cover Mapping With Multi-Resolution Data.

Caleb Robinson, Le Hou, Kolya Malkin, Rachel Soobitsky, Jacob Czawlytko, Bistra Dilkina, Nebojsa Jojic

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

Robust Histopathology Image Analysis: To Label or to Synthesize?

Le Hou, Ayush Agarwal, Dimitris Samaras, Tahsin M. Kurç, Rajarsi R. Gupta, Joel H. Saltz

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

From Whole Slide Tissues to Knowledge: Mapping Sub-cellular Morphology of Cancer.

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

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2019

Exascale Deep Learning to Accelerate Cancer Research.

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

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2017

Unsupervised Histopathology Image Synthesis.

Le Hou, Ayush Agarwal, Dimitris Samaras, Tahsin M. Kurç, Rajarsi R. Gupta, Joel H. Saltz

CoRR, 2017

Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images.

Le Hou, Vu Nguyen, Dimitris Samaras, Tahsin M. Kurç, Yi Gao, Tianhao Zhao, Joel H. Saltz

CoRR, 2017

Center-Focusing Multi-task CNN with Injected Features for Classification of Glioma Nuclear Images.

Veda Murthy, Le Hou, Dimitris Samaras, Tahsin M. Kurç, Joel H. Saltz

Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision, 2017

ConvNets with Smooth Adaptive Activation Functions for Regression.

Le Hou, Dimitris Samaras, Tahsin M. Kurç, Yi Gao, Joel H. Saltz

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks.

Le Hou, Chen-Ping Yu, Dimitris Samaras

CoRR, 2016

Neural Networks with Smooth Adaptive Activation Functions for Regression.

Le Hou, Dimitris Samaras, Tahsin M. Kurç, Yi Gao, Joel H. Saltz

CoRR, 2016

Large-Scale Training of Shadow Detectors with Noisily-Annotated Shadow Examples.

Tomás F. Yago Vicente, Le Hou, Chen-Ping Yu, Minh Hoai, Dimitris Samaras

Proceedings of the Computer Vision - ECCV 2016, 2016

Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification.

Le Hou, Dimitris Samaras, Tahsin M. Kurç, Yi Gao, James E. Davis, Joel H. Saltz

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

2015

Efficient Multiple Instance Convolutional Neural Networks for Gigapixel Resolution Image Classification.

Le Hou, Dimitris Samaras, Tahsin M. Kurç, Yi Gao, James E. Davis, Joel H. Saltz

CoRR, 2015