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 Eric Xing

Eric Xing

Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography


Feb 24, 2021
Xuefeng Du, Haohan Wang, Zhenxi Zhu, Xiangrui Zeng, Yi-Wei Chang, Jing Zhang, Eric Xing, Min Xu

* 14 ages 
  Access Paper or Ask Questions

On Data Efficiency of Meta-learning


Jan 30, 2021
Maruan Al-Shedivat, Liam Li, Eric Xing, Ameet Talwalkar

Figure 1 for On Data Efficiency of Meta-learning
Figure 2 for On Data Efficiency of Meta-learning
Figure 3 for On Data Efficiency of Meta-learning
Figure 4 for On Data Efficiency of Meta-learning
* Preliminary version. An updated version is to appear in AISTATS 2021 
  Access Paper or Ask Questions

Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling


Dec 11, 2020
Benedikt Boecking, Willie Neiswanger, Eric Xing, Artur Dubrawski

Figure 1 for Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
Figure 2 for Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
Figure 3 for Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
Figure 4 for Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling

  Access Paper or Ask Questions

Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms


Oct 11, 2020
Maruan Al-Shedivat, Jennifer Gillenwater, Eric Xing, Afshin Rostamizadeh

Figure 1 for Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Figure 2 for Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Figure 3 for Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
Figure 4 for Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
* 27 pages, 7 figures 
  Access Paper or Ask Questions

Pathological Visual Question Answering


Oct 06, 2020
Xuehai He, Zhuo Cai, Wenlan Wei, Yichen Zhang, Luntian Mou, Eric Xing, Pengtao Xie

Figure 1 for Pathological Visual Question Answering
Figure 2 for Pathological Visual Question Answering
Figure 3 for Pathological Visual Question Answering
Figure 4 for Pathological Visual Question Answering
* arXiv admin note: text overlap with arXiv:2003.10286 
  Access Paper or Ask Questions

XRayGAN: Consistency-preserving Generation of X-ray Images from Radiology Reports


Jun 17, 2020
Xingyi Yang, Nandiraju Gireesh, Eric Xing, Pengtao Xie

Figure 1 for XRayGAN: Consistency-preserving Generation of X-ray Images from Radiology Reports
Figure 2 for XRayGAN: Consistency-preserving Generation of X-ray Images from Radiology Reports
Figure 3 for XRayGAN: Consistency-preserving Generation of X-ray Images from Radiology Reports
Figure 4 for XRayGAN: Consistency-preserving Generation of X-ray Images from Radiology Reports

  Access Paper or Ask Questions

On the Generation of Medical Dialogues for COVID-19


May 11, 2020
Wenmian Yang, Guangtao Zeng, Bowen Tan, Zeqian Ju, Subrato Chakravorty, Xuehai He, Shu Chen, Xingyi Yang, Qingyang Wu, Zhou Yu, Eric Xing, Pengtao Xie

Figure 1 for On the Generation of Medical Dialogues for COVID-19
Figure 2 for On the Generation of Medical Dialogues for COVID-19
Figure 3 for On the Generation of Medical Dialogues for COVID-19
Figure 4 for On the Generation of Medical Dialogues for COVID-19

  Access Paper or Ask Questions

Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports


Apr 26, 2020
Baoyu Jing, Zeya Wang, Eric Xing

Figure 1 for Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports
Figure 2 for Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports
Figure 3 for Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports
Figure 4 for Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports
* ACL 2019 
  Access Paper or Ask Questions

Learning from Imperfect Annotations


Apr 07, 2020
Emmanouil Antonios Platanios, Maruan Al-Shedivat, Eric Xing, Tom Mitchell

Figure 1 for Learning from Imperfect Annotations
Figure 2 for Learning from Imperfect Annotations
Figure 3 for Learning from Imperfect Annotations
Figure 4 for Learning from Imperfect Annotations

  Access Paper or Ask Questions

PathVQA: 30000+ Questions for Medical Visual Question Answering


Mar 07, 2020
Xuehai He, Yichen Zhang, Luntian Mou, Eric Xing, Pengtao Xie

Figure 1 for PathVQA: 30000+ Questions for Medical Visual Question Answering
Figure 2 for PathVQA: 30000+ Questions for Medical Visual Question Answering
Figure 3 for PathVQA: 30000+ Questions for Medical Visual Question Answering
Figure 4 for PathVQA: 30000+ Questions for Medical Visual Question Answering

  Access Paper or Ask Questions

Generalized Zero-shot ICD Coding


Sep 28, 2019
Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, Eric Xing

Figure 1 for Generalized Zero-shot ICD Coding
Figure 2 for Generalized Zero-shot ICD Coding
Figure 3 for Generalized Zero-shot ICD Coding
Figure 4 for Generalized Zero-shot ICD Coding

  Access Paper or Ask Questions

Efficient Exploration via State Marginal Matching


Jun 12, 2019
Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric Xing, Sergey Levine, Ruslan Salakhutdinov

Figure 1 for Efficient Exploration via State Marginal Matching
Figure 2 for Efficient Exploration via State Marginal Matching
Figure 3 for Efficient Exploration via State Marginal Matching
Figure 4 for Efficient Exploration via State Marginal Matching
* Videos and code: https://sites.google.com/view/state-marginal-matching 
  Access Paper or Ask Questions

Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)


May 31, 2019
Gregory Plumb, Maruan Al-Shedivat, Eric Xing, Ameet Talwalkar

Figure 1 for Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)
Figure 2 for Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)
Figure 3 for Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)
Figure 4 for Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)
* presented at 2019 ICML Workshop on Human in the Loop Learning (HILL 2019), Long Beach, USA. arXiv admin note: substantial text overlap with arXiv:1902.06787 
  Access Paper or Ask Questions

SysML: The New Frontier of Machine Learning Systems


May 01, 2019
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar


  Access Paper or Ask Questions

Explaining a black-box using Deep Variational Information Bottleneck Approach


Feb 19, 2019
Seojin Bang, Pengtao Xie, Wei Wu, Eric Xing

Figure 1 for Explaining a black-box using Deep Variational Information Bottleneck Approach
Figure 2 for Explaining a black-box using Deep Variational Information Bottleneck Approach
Figure 3 for Explaining a black-box using Deep Variational Information Bottleneck Approach
Figure 4 for Explaining a black-box using Deep Variational Information Bottleneck Approach

  Access Paper or Ask Questions

Regularizing Black-box Models for Improved Interpretability


Feb 18, 2019
Gregory Plumb, Maruan Al-Shedivat, Eric Xing, Ameet Talwalkar

Figure 1 for Regularizing Black-box Models for Improved Interpretability
Figure 2 for Regularizing Black-box Models for Improved Interpretability
Figure 3 for Regularizing Black-box Models for Improved Interpretability
Figure 4 for Regularizing Black-box Models for Improved Interpretability

  Access Paper or Ask Questions

Toward Unsupervised Text Content Manipulation


Feb 08, 2019
Wentao Wang, Zhiting Hu, Zichao Yang, Haoran Shi, Frank Xu, Eric Xing

Figure 1 for Toward Unsupervised Text Content Manipulation
Figure 2 for Toward Unsupervised Text Content Manipulation
Figure 3 for Toward Unsupervised Text Content Manipulation
Figure 4 for Toward Unsupervised Text Content Manipulation
* The first 2 authors contributed equally. Dataset is released at https://github.com/ZhitingHu/text_content_manipulation 
  Access Paper or Ask Questions

ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization


Jan 31, 2019
Willie Neiswanger, Kirthevasan Kandasamy, Barnabas Poczos, Jeff Schneider, Eric Xing

Figure 1 for ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization
Figure 2 for ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization
Figure 3 for ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization
Figure 4 for ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization

  Access Paper or Ask Questions

Text Infilling


Jan 18, 2019
Wanrong Zhu, Zhiting Hu, Eric Xing

Figure 1 for Text Infilling
Figure 2 for Text Infilling
Figure 3 for Text Infilling
Figure 4 for Text Infilling

  Access Paper or Ask Questions

Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records


Nov 26, 2018
Xiangan Liu, Keyang Xu, Pengtao Xie, Eric Xing

Figure 1 for Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records
Figure 2 for Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records
* Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216 
  Access Paper or Ask Questions

Connecting the Dots Between MLE and RL for Sequence Generation


Nov 24, 2018
Bowen Tan, Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric Xing

Figure 1 for Connecting the Dots Between MLE and RL for Sequence Generation
Figure 2 for Connecting the Dots Between MLE and RL for Sequence Generation
Figure 3 for Connecting the Dots Between MLE and RL for Sequence Generation
Figure 4 for Connecting the Dots Between MLE and RL for Sequence Generation
* The first two authors contributed equally. Code available at: https://github.com/asyml/texar/tree/master/examples/seq2seq_exposure_bias 
  Access Paper or Ask Questions

On the Complexity of Exploration in Goal-Driven Navigation


Nov 16, 2018
Maruan Al-Shedivat, Lisa Lee, Ruslan Salakhutdinov, Eric Xing

Figure 1 for On the Complexity of Exploration in Goal-Driven Navigation
* Relational Representation Learning Workshop (NIPS 2018) 
  Access Paper or Ask Questions

Multimodal Machine Learning for Automated ICD Coding


Oct 31, 2018
Keyang Xu, Mike Lam, Jingzhi Pang, Xin Gao, Charlotte Band, Piyush Mathur MD, Frank Papay MD, Ashish K. Khanna MD, Jacek B. Cywinski MD, Kamal Maheshwari MD, Pengtao Xie, Eric Xing

Figure 1 for Multimodal Machine Learning for Automated ICD Coding
Figure 2 for Multimodal Machine Learning for Automated ICD Coding
Figure 3 for Multimodal Machine Learning for Automated ICD Coding
Figure 4 for Multimodal Machine Learning for Automated ICD Coding

  Access Paper or Ask Questions

Real-to-Virtual Domain Unification for End-to-End Autonomous Driving


Sep 06, 2018
Luona Yang, Xiaodan Liang, Tairui Wang, Eric Xing

Figure 1 for Real-to-Virtual Domain Unification for End-to-End Autonomous Driving
Figure 2 for Real-to-Virtual Domain Unification for End-to-End Autonomous Driving
Figure 3 for Real-to-Virtual Domain Unification for End-to-End Autonomous Driving
Figure 4 for Real-to-Virtual Domain Unification for End-to-End Autonomous Driving

  Access Paper or Ask Questions

Missing Value Imputation Based on Deep Generative Models


Aug 05, 2018
Hongbao Zhang, Pengtao Xie, Eric Xing

Figure 1 for Missing Value Imputation Based on Deep Generative Models
Figure 2 for Missing Value Imputation Based on Deep Generative Models
Figure 3 for Missing Value Imputation Based on Deep Generative Models
Figure 4 for Missing Value Imputation Based on Deep Generative Models

  Access Paper or Ask Questions

On the Automatic Generation of Medical Imaging Reports


Jul 20, 2018
Baoyu Jing, Pengtao Xie, Eric Xing

Figure 1 for On the Automatic Generation of Medical Imaging Reports
Figure 2 for On the Automatic Generation of Medical Imaging Reports
Figure 3 for On the Automatic Generation of Medical Imaging Reports
Figure 4 for On the Automatic Generation of Medical Imaging Reports
* ACL 2018 
  Access Paper or Ask Questions

Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful


Jul 11, 2018
Rajesh Chidambaram, Michael Kampffmeyer, Willie Neiswanger, Xiaodan Liang, Thomas Lachmann, Eric Xing

Figure 1 for Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful
Figure 2 for Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful
Figure 3 for Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful
Figure 4 for Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful
* ICML 2018, Workshop TADGM 
  Access Paper or Ask Questions

CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving


Jul 10, 2018
Xiaodan Liang, Tairui Wang, Luona Yang, Eric Xing

Figure 1 for CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving
Figure 2 for CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving
Figure 3 for CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving
Figure 4 for CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving
* To appear in ECCV 2018 
  Access Paper or Ask Questions