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2020 – today
- 2023
[c12]Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu:
Out-of-Distribution Detection and Selective Generation for Conditional Language Models. ICLR 2023- [c11]Reinald Kim Amplayo, Peter J. Liu, Yao Zhao, Shashi Narayan:
SMART: Sentences as Basic Units for Text Evaluation. ICLR 2023 - [c10]Yao Zhao, Misha Khalman, Rishabh Joshi, Shashi Narayan, Mohammad Saleh, Peter J. Liu:
Calibrating Sequence likelihood Improves Conditional Language Generation. ICLR 2023 - [i25]Yao Zhao, Rishabh Joshi, Tianqi Liu, Misha Khalman, Mohammad Saleh, Peter J. Liu:
SLiC-HF: Sequence Likelihood Calibration with Human Feedback. CoRR abs/2305.10425 (2023) - [i24]Tianqi Liu, Yao Zhao, Rishabh Joshi, Misha Khalman, Mohammad Saleh, Peter J. Liu, Jialu Liu:
Statistical Rejection Sampling Improves Preference Optimization. CoRR abs/2309.06657 (2023) - [i23]Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie Everett, Alex Alemi, Ben Adlam, John D. Co-Reyes, Izzeddin Gur, Abhishek Kumar, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein, Kelvin Xu, Jaehoon Lee, Justin Gilmer, Simon Kornblith:
Small-scale proxies for large-scale Transformer training instabilities. CoRR abs/2309.14322 (2023) - [i22]Yixin Liu, Avi Singh, C. Daniel Freeman, John D. Co-Reyes, Peter J. Liu:
Improving Large Language Model Fine-tuning for Solving Math Problems. CoRR abs/2310.10047 (2023) - [i21]C. Daniel Freeman, Laura Culp, Aaron Parisi, Maxwell L. Bileschi, Gamaleldin F. Elsayed, Alex Rizkowsky, Isabelle Simpson, Alex Alemi, Azade Nova, Ben Adlam, Bernd Bohnet, Gaurav Mishra, Hanie Sedghi, Igor Mordatch, Izzeddin Gur, Jaehoon Lee, John D. Co-Reyes, Jeffrey Pennington, Kelvin Xu, Kevin Swersky, Kshiteej Mahajan, Lechao Xiao, Rosanne Liu, Simon Kornblith, Noah Constant, Peter J. Liu, Roman Novak, Yundi Qian, Noah Fiedel, Jascha Sohl-Dickstein:
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5? CoRR abs/2311.07587 (2023) - 2022
- [i20]Reinald Kim Amplayo, Peter J. Liu, Yao Zhao, Shashi Narayan:
SMART: Sentences as Basic Units for Text Evaluation. CoRR abs/2208.01030 (2022) - [i19]Jason Phang, Yao Zhao, Peter J. Liu:
Investigating Efficiently Extending Transformers for Long Input Summarization. CoRR abs/2208.04347 (2022) - [i18]Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, Peter J. Liu:
Out-of-Distribution Detection and Selective Generation for Conditional Language Models. CoRR abs/2209.15558 (2022) - [i17]Yao Zhao, Misha Khalman, Rishabh Joshi
, Shashi Narayan, Mohammad Saleh, Peter J. Liu:
Calibrating Sequence likelihood Improves Conditional Language Generation. CoRR abs/2210.00045 (2022) - [i16]Kundan Krishna, Yao Zhao, Jie Ren, Balaji Lakshminarayanan, Jiaming Luo, Mohammad Saleh, Peter J. Liu:
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise. CoRR abs/2212.09928 (2022) - 2020
- [j2]Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu:
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. J. Mach. Learn. Res. 21: 140:1-140:67 (2020) - [c9]Jingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. Liu:
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. ICML 2020: 11328-11339 - [i15]Yao Zhao, Mohammad Saleh, Peter J. Liu:
SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization. CoRR abs/2006.10213 (2020)
2010 – 2019
- 2019
- [c8]Eric Chu, Peter J. Liu:
MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization. ICML 2019: 1223-1232 - [c7]Ben Goodrich, Vinay Rao, Peter J. Liu, Mohammad Saleh:
Assessing The Factual Accuracy of Generated Text. KDD 2019: 166-175 - [c6]Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan:
Likelihood Ratios for Out-of-Distribution Detection. NeurIPS 2019: 14680-14691 - [i14]Ethan Steinberg, Peter J. Liu:
Using Ontologies To Improve Performance In Massively Multi-label Prediction Models. CoRR abs/1905.12126 (2019) - [i13]Ben Goodrich, Vinay Rao, Mohammad Saleh, Peter J. Liu:
Assessing The Factual Accuracy of Generated Text. CoRR abs/1905.13322 (2019) - [i12]Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan:
Likelihood Ratios for Out-of-Distribution Detection. CoRR abs/1906.02845 (2019) - [i11]Peter J. Liu, Yu-An Chung, Jie Ren:
SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders. CoRR abs/1910.00998 (2019) - [i10]Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu:
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. CoRR abs/1910.10683 (2019) - [i9]Jingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. Liu:
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. CoRR abs/1912.08777 (2019) - 2018
- [j1]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) - [c5]Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer:
Generating Wikipedia by Summarizing Long Sequences. ICLR (Poster) 2018 - [c4]W. James Murdoch, Peter J. Liu, Bin Yu:
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs. ICLR 2018 - [i8]W. James Murdoch, Peter J. Liu, Bin Yu:
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs. CoRR abs/1801.05453 (2018) - [i7]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) - [i6]Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer:
Generating Wikipedia by Summarizing Long Sequences. CoRR abs/1801.10198 (2018) - [i5]Peter J. Liu:
Learning to Write Notes in Electronic Health Records. CoRR abs/1808.02622 (2018) - [i4]Eric Chu, Peter J. Liu:
Unsupervised Neural Multi-document Abstractive Summarization. CoRR abs/1810.05739 (2018) - 2017
- [c3]Abigail See, Peter J. Liu, Christopher D. Manning:
Get To The Point: Summarization with Pointer-Generator Networks. ACL (1) 2017: 1073-1083 - [c2]Prajit Ramachandran, Peter J. Liu, Quoc V. Le:
Unsupervised Pretraining for Sequence to Sequence Learning. EMNLP 2017: 383-391 - [c1]Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck:
Online and Linear-Time Attention by Enforcing Monotonic Alignments. ICML 2017: 2837-2846 - [i3]Colin Raffel, Minh-Thang Luong, Peter J. Liu, Ron J. Weiss, Douglas Eck:
Online and Linear-Time Attention by Enforcing Monotonic Alignments. CoRR abs/1704.00784 (2017) - [i2]Abigail See, Peter J. Liu, Christopher D. Manning:
Get To The Point: Summarization with Pointer-Generator Networks. CoRR abs/1704.04368 (2017) - 2016
- [i1]Prajit Ramachandran, Peter J. Liu, Quoc V. Le:
Unsupervised Pretraining for Sequence to Sequence Learning. CoRR abs/1611.02683 (2016)
Coauthor Index
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last updated on 2023-11-22 20:42 CET by the dblp team
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