Edward J. Hu

According to our database, Edward J. Hu authored at least 22 papers between 2018 and 2023.

Collaborative distances:

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

2023

GFlowNet Foundations.

Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, Emmanuel Bengio

J. Mach. Learn. Res., 2023

Amortizing intractable inference in large language models.

Edward J. Hu, Moksh Jain, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, Nikolay Malkin

CoRR, 2023

Differentiable Tree Operations Promote Compositional Generalization.

Paul Soulos, Edward J. Hu, Kate McCurdy, Yunmo Chen, Roland Fernandez, Paul Smolensky, Jianfeng Gao

Proceedings of the International Conference on Machine Learning, 2023

GFlowNet-EM for Learning Compositional Latent Variable Models.

Edward J. Hu, Nikolay Malkin, Moksh Jain, Katie E. Everett, Alexandros Graikos, Yoshua Bengio

Proceedings of the International Conference on Machine Learning, 2023

GFlowNets and variational inference.

Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward J. Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer.

Greg Yang, Edward J. Hu, Igor Babuschkin, Szymon Sidor, Xiaodong Liu, David Farhi, Nick Ryder, Jakub Pachocki, Weizhu Chen, Jianfeng Gao

CoRR, 2022

Efficient Computation of Deep Nonlinear Infinite-Width Neural Networks that Learn Features.

Greg Yang, Michael Santacroce, Edward J. Hu

Proceedings of the Tenth International Conference on Learning Representations, 2022

LoRA: Low-Rank Adaptation of Large Language Models.

Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Iterative Paraphrastic Augmentation with Discriminative Span Alignment.

Ryan Culkin, J. Edward Hu, Elias Stengel-Eskin, Guanghui Qin, Benjamin Van Durme

Trans. Assoc. Comput. Linguistics, 2021

LoRA: Low-Rank Adaptation of Large Language Models.

Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Weizhu Chen

CoRR, 2021

Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer.

Ge Yang, Edward J. Hu, Igor Babuschkin, Szymon Sidor, Xiaodong Liu, David Farhi, Nick Ryder, Jakub Pachocki, Weizhu Chen, Jianfeng Gao

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks.

Greg Yang, Edward J. Hu

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Feature Learning in Infinite-Width Neural Networks.

Greg Yang, J. Edward Hu

CoRR, 2020

Improved Image Wasserstein Attacks and Defenses.

J. Edward Hu, Adith Swaminathan, Hadi Salman, Greg Yang

CoRR, 2020

Guided Generation of Cause and Effect.

Zhongyang Li, Xiao Ding, Ting Liu, J. Edward Hu, Benjamin Van Durme

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Randomized Smoothing of All Shapes and Sizes.

Greg Yang, Tony Duan, J. Edward Hu, Hadi Salman, Ilya P. Razenshteyn, Jerry Li

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

NIST TAC SM-KBP 2019 System Description: JHU/UR Framework.

Yunmo Chen, Seth Ebner, Tongfei Chen, Patrick Xia, Elias Stengel-Eskin, Tzu-Ray Su, J. Edward Hu, Nils Holzenberger, Ryan Culkin, Craig Harman, Max Thomas, Thomas Lippincott, Aaron Steven White, Kyle Rawlins, Benjamin Van Durme

Proceedings of the 2019 Text Analysis Conference, 2019

Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting.

J. Edward Hu, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, Benjamin Van Durme

Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019

Large-Scale, Diverse, Paraphrastic Bitexts via Sampling and Clustering.

J. Edward Hu, Abhinav Singh, Nils Holzenberger, Matt Post, Benjamin Van Durme

Proceedings of the 23rd Conference on Computational Natural Language Learning, 2019

PARABANK: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-Constrained Neural Machine Translation.

J. Edward Hu, Rachel Rudinger, Matt Post, Benjamin Van Durme

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Towards a Unified Natural Language Inference Framework to Evaluate Sentence Representations.

Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme

CoRR, 2018

Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation.

Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018