Pengtao Xie

is this you? claim profile

∙ 1 follower
Petuum, Inc. ∙ Carnegie Mellon University ∙ University of California, San Diego

Director of Data Solutions and Service, Research Scientist, at Petuum Inc., Research Scientist, at Petuum Inc., Graduate Research Assistant at Carnegie Mellon University from 2013-2018, Research Intern at Microsoft Research (Redmond) 2014, Visiting Student at Carnegie Mellon University from 2011-2013, Intern at Microsoft Research Asia from 2009-2010

Featured Co-authors

Graham Neubig
165 publications
Ruslan Salakhutdinov
149 publications
Jun Zhu
143 publications
Dong Wang
132 publications
Eric P Xing
126 publications
Xiaodan Liang
112 publications
Wei Wu
97 publications
Zhou Yu
70 publications
Shuai Zhang
59 publications
Hao Zhang
58 publications
Eric Xing
49 publications

Interleaving Learning, with Application to Neural Architecture Search

Interleaving learning is a human learning technique where a learner inte...
03/12/2021 ∙ by Hao Ban, et al. ∙ 0 ∙ share

read it
Learning by Teaching, with Application to Neural Architecture Search

In human learning, an effective skill in improving learning outcomes is ...
03/11/2021 ∙ by Parth Sheth, et al. ∙ 0 ∙ share

read it
Self-supervised Regularization for Text Classification

Text classification is a widely studied problem and has broad applicatio...
03/09/2021 ∙ by Meng Zhou, et al. ∙ 0 ∙ share

read it
Learning by Ignoring

Learning by ignoring, which identifies less important things and exclude...
12/28/2020 ∙ by Xingchen Zhao, et al. ∙ 0 ∙ share

read it
Learning by Self-Explanation, with Application to Neural Architecture Search

Learning by self-explanation, where students explain a learned topic to ...
12/23/2020 ∙ by Ramtin Hosseini, et al. ∙ 0 ∙ share

read it
Small-Group Learning, with Application to Neural Architecture Search

Small-group learning is a broadly used methodology in human learning and...
12/23/2020 ∙ by Xuefeng Du, et al. ∙ 0 ∙ share

read it
DSRNA: Differentiable Search of Robust Neural Architectures

In deep learning applications, the architectures of deep neural networks...
12/11/2020 ∙ by Ramtin Hosseini, et al. ∙ 0 ∙ share

read it
Skillearn: Machine Learning Inspired by Humans' Learning Skills

Humans, as the most powerful learners on the planet, have accumulated a ...
12/09/2020 ∙ by Pengtao Xie, et al. ∙ 0 ∙ share

read it
Learning by Passing Tests, with Application to Neural Architecture Search

Learning through tests is a broadly used methodology in human learning a...
11/30/2020 ∙ by Xuefeng Du, et al. ∙ 0 ∙ share

read it
Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications

While deep learning methods have shown great success in medical image an...
10/07/2020 ∙ by Yue Yang, et al. ∙ 4 ∙ share

read it
TreeGAN: Incorporating Class Hierarchy into Image Generation

Conditional image generation (CIG) is a widely studied problem in comput...
09/16/2020 ∙ by Ruisi Zhang, et al. ∙ 41 ∙ share

read it
Contrastive Self-supervised Learning for Graph Classification

Graph classification is a widely studied problem and has broad applicati...
09/13/2020 ∙ by Jiaqi Zeng, et al. ∙ 29 ∙ share

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

To effectively train medical students to become qualified radiologists, ...
06/17/2020 ∙ by Xingyi Yang, et al. ∙ 20 ∙ share

read it
Differentially-private Federated Neural Architecture Search

Neural architecture search, which aims to automatically search for archi...
06/16/2020 ∙ by Ishika Singh, et al. ∙ 0 ∙ share

read it
CERT: Contrastive Self-supervised Learning for Language Understanding

Pretrained language models such as BERT, GPT have shown great effectiven...
05/16/2020 ∙ by Hongchao Fang, et al. ∙ 0 ∙ share

read it
On the Generation of Medical Dialogues for COVID-19

Under the pandemic of COVID-19, people experiencing COVID19-related symp...
05/11/2020 ∙ by Wenmian Yang, et al. ∙ 0 ∙ share

read it
MedDialog: A Large-scale Medical Dialogue Dataset

Medical dialogue systems are promising in assisting in telemedicine to i...
04/07/2020 ∙ by Shu Chen, et al. ∙ 9 ∙ share

read it
Identifying Radiological Findings Related to COVID-19 from Medical Literature

Coronavirus disease 2019 (COVID-19) has infected more than one million i...
04/04/2020 ∙ by Yuxiao Liang, et al. ∙ 0 ∙ share

read it
COVID-CT-Dataset: A CT Scan Dataset about COVID-19

CT scans are promising in providing accurate, fast, and cheap screening ...
03/30/2020 ∙ by Jinyu Zhao, et al. ∙ 78 ∙ share

read it
PathVQA: 30000+ Questions for Medical Visual Question Answering

Is it possible to develop an "AI Pathologist" to pass the board-certifie...
03/07/2020 ∙ by Xuehai He, et al. ∙ 0 ∙ share

read it
Generalized Zero-shot ICD Coding

The International Classification of Diseases (ICD) is a list of classifi...
09/28/2019 ∙ by Congzheng Song, et al. ∙ 0 ∙ share

read it
Adversarial Domain Adaptation Being Aware of Class Relationships

Adversarial training is a useful approach to promote the learning of tra...
05/28/2019 ∙ by Zeya Wang, et al. ∙ 1 ∙ share

read it
Explaining a black-box using Deep Variational Information Bottleneck Approach

Briefness and comprehensiveness are necessary in order to give a lot of ...
02/19/2019 ∙ by Seojin Bang, et al. ∙ 8 ∙ share

read it
Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records

Extractive summarization is very useful for physicians to better manage ...
11/20/2018 ∙ by Xiangan Liu, et al. ∙ 0 ∙ share

read it
Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures

We study the problem of alleviating the instability issue in the GAN tra...
11/19/2018 ∙ by Hongyang Zhang, et al. ∙ 8 ∙ share

read it
Multimodal Machine Learning for Automated ICD Coding

This study presents a multimodal machine learning model to predict ICD-1...
10/31/2018 ∙ by Keyang Xu, et al. ∙ 4 ∙ share

read it
Missing Value Imputation Based on Deep Generative Models

Missing values widely exist in many real-world datasets, which hinders t...
08/05/2018 ∙ by Hongbao Zhang, et al. ∙ 14 ∙ share

read it
Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis

Distance metric learning (DML), which learns a distance metric from labe...
02/16/2018 ∙ by Pengtao Xie, et al. ∙ 0 ∙ share

read it
Convolutional Neural Networks for Medical Diagnosis from Admission Notes

Objective Develop an automatic diagnostic system which only uses textual...
12/06/2017 ∙ by Christy Li, et al. ∙ 0 ∙ share

read it
Learning Less-Overlapping Representations

In representation learning (RL), how to make the learned representations...
11/25/2017 ∙ by Pengtao Xie, et al. ∙ 0 ∙ share

read it
Stacked Kernel Network

Kernel methods are powerful tools to capture nonlinear patterns behind d...
11/25/2017 ∙ by Shuai Zhang, et al. ∙ 0 ∙ share

read it
Diversity-Promoting Bayesian Learning of Latent Variable Models

To address three important issues involved in latent variable models (LV...
11/23/2017 ∙ by Pengtao Xie, et al. ∙ 0 ∙ share

read it
On the Automatic Generation of Medical Imaging Reports

Medical imaging is widely used in clinical practice for diagnosis and tr...
11/22/2017 ∙ by Baoyu Jing, et al. ∙ 0 ∙ share

read it
Effective Use of Bidirectional Language Modeling for Medical Named Entity Recognition

Biomedical named entity recognition (NER) is a fundamental task in text ...
11/21/2017 ∙ by Devendra Singh Sachan, et al. ∙ 0 ∙ share

read it
Medical Diagnosis From Laboratory Tests by Combining Generative and Discriminative Learning

A primary goal of computational phenotype research is to conduct medical...
11/12/2017 ∙ by Shiyue Zhang, et al. ∙ 0 ∙ share

read it
Towards Automated ICD Coding Using Deep Learning

International Classification of Diseases(ICD) is an authoritative health...
11/11/2017 ∙ by Haoran Shi, et al. ∙ 0 ∙ share

read it
Predicting Discharge Medications at Admission Time Based on Deep Learning

Predicting discharge medications right after a patient being admitted is...
11/04/2017 ∙ by Yuan Yang, et al. ∙ 0 ∙ share

read it
Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters

Deep learning models can take weeks to train on a single GPU-equipped ma...
06/11/2017 ∙ by Hao Zhang, et al. ∙ 0 ∙ share

read it
Strategies and Principles of Distributed Machine Learning on Big Data

The rise of Big Data has led to new demands for Machine Learning (ML) sy...
12/31/2015 ∙ by Eric P Xing, et al. ∙ 0 ∙ share

read it
Latent Variable Modeling with Diversity-Inducing Mutual Angular Regularization

Latent Variable Models (LVMs) are a large family of machine learning mod...
12/23/2015 ∙ by Pengtao Xie, et al. ∙ 0 ∙ share

read it
Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines

Deep learning (DL) has achieved notable successes in many machine learni...
12/19/2015 ∙ by Hao Zhang, et al. ∙ 0 ∙ share

read it
Distributed Training of Deep Neural Networks with Theoretical Analysis: Under SSP Setting

We propose a distributed approach to train deep neural networks (DNNs), ...
12/09/2015 ∙ by Abhimanu Kumar, et al. ∙ 0 ∙ share

read it
Cauchy Principal Component Analysis

Principal Component Analysis (PCA) has wide applications in machine lear...
12/19/2014 ∙ by Pengtao Xie, et al. ∙ 0 ∙ share

read it
Crypto-Nets: Neural Networks over Encrypted Data

The problem we address is the following: how can a user employ a predict...
12/18/2014 ∙ by Pengtao Xie, et al. ∙ 0 ∙ share

read it
Petuum: A New Platform for Distributed Machine Learning on Big Data

What is a systematic way to efficiently apply a wide spectrum of advance...
12/30/2013 ∙ by Eric P Xing, et al. ∙ 0 ∙ share

read it
Integrating Document Clustering and Topic Modeling

Document clustering and topic modeling are two closely related tasks whi...
09/26/2013 ∙ by Pengtao Xie, et al. ∙ 0 ∙ share

read it