Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox
Picture for Peter J. Liu

Peter J. Liu

SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization



Yao Zhao , Mohammad Saleh , Peter J. Liu

Figure 1 for SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization
Figure 2 for SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization
Figure 3 for SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization
Figure 4 for SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization

   Access Paper or Ask Questions

PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization



Jingqing Zhang , Yao Zhao , Mohammad Saleh , Peter J. Liu

Figure 1 for PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Figure 2 for PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Figure 3 for PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Figure 4 for PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization

   Access Paper or Ask Questions

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer



Colin Raffel , Noam Shazeer , Adam Roberts , Katherine Lee , Sharan Narang , Michael Matena , Yanqi Zhou , Wei Li , Peter J. Liu

Figure 1 for Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Figure 2 for Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Figure 3 for Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Figure 4 for Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

   Access Paper or Ask Questions

SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders



Peter J. Liu , Yu-An Chung , Jie Ren

Figure 1 for SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders
Figure 2 for SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders
Figure 3 for SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders
Figure 4 for SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders
* first two authors contributed equally 
   Access Paper or Ask Questions

Likelihood Ratios for Out-of-Distribution Detection



Jie Ren , Peter J. Liu , Emily Fertig , Jasper Snoek , Ryan Poplin , Mark A. DePristo , Joshua V. Dillon , Balaji Lakshminarayanan

Figure 1 for Likelihood Ratios for Out-of-Distribution Detection
Figure 2 for Likelihood Ratios for Out-of-Distribution Detection
Figure 3 for Likelihood Ratios for Out-of-Distribution Detection
Figure 4 for Likelihood Ratios for Out-of-Distribution Detection

   Access Paper or Ask Questions

Using Ontologies To Improve Performance In Massively Multi-label Prediction Models



Ethan Steinberg , Peter J. Liu

Figure 1 for Using Ontologies To Improve Performance In Massively Multi-label Prediction Models
Figure 2 for Using Ontologies To Improve Performance In Massively Multi-label Prediction Models
Figure 3 for Using Ontologies To Improve Performance In Massively Multi-label Prediction Models
Figure 4 for Using Ontologies To Improve Performance In Massively Multi-label Prediction Models

   Access Paper or Ask Questions

Unsupervised Neural Multi-document Abstractive Summarization



Eric Chu , Peter J. Liu

Figure 1 for Unsupervised Neural Multi-document Abstractive Summarization
Figure 2 for Unsupervised Neural Multi-document Abstractive Summarization
Figure 3 for Unsupervised Neural Multi-document Abstractive Summarization
Figure 4 for Unsupervised Neural Multi-document Abstractive Summarization
* preprint 
   Access Paper or Ask Questions

Learning to Write Notes in Electronic Health Records



Peter J. Liu

Figure 1 for Learning to Write Notes in Electronic Health Records
Figure 2 for Learning to Write Notes in Electronic Health Records
Figure 3 for Learning to Write Notes in Electronic Health Records
Figure 4 for Learning to Write Notes in Electronic Health Records
* preprint 
   Access Paper or Ask Questions

Scalable and accurate deep learning for electronic health records



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 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 Howell , Claire Cui , Greg Corrado , Jeff Dean

Figure 1 for Scalable and accurate deep learning for electronic health records
* npj Digital Medicine 1:18 (2018) 
 * Published version from https://www.nature.com/articles/s41746-018-0029-1 
   Access Paper or Ask Questions

Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs



W. James Murdoch , Peter J. Liu , Bin Yu

Figure 1 for Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
Figure 2 for Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
Figure 3 for Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
Figure 4 for Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
* Oral presentation at ICLR 2018 
   Access Paper or Ask Questions
1
2
>>