b'Pengtao Xie'

research

∙ 02/16/2022

Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations

Vision Transformers (ViTs) take all the image patches as tokens and cons...

Youwei Liang, et al. ∙

research

∙ 01/04/2022

Self-directed Machine Learning

Conventional machine learning (ML) relies heavily on manual design from ...

Wenwu Zhu, et al. ∙

research

∙ 12/01/2021

Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search

Learning from mistakes is an effective learning approach widely used in ...

Jay Gala, et al. ∙

research

∙ 11/30/2021

Improving Differentiable Architecture Search with a Generative Model

In differentiable neural architecture search (NAS) algorithms like DARTS...

Ruisi Zhang, et al. ∙

research

∙ 11/11/2021

Learning from Mistakes – A Framework for Neural Architecture Search

Learning from one's mistakes is an effective human learning technique wh...

Bhanu Garg, et al. ∙

research

∙ 09/22/2021

Learning by Examples Based on Multi-level Optimization

Learning by examples, which learns to solve a new problem by looking int...

Shentong Mo, et al. ∙

research

∙ 03/12/2021

Interleaving Learning, with Application to Neural Architecture Search

Interleaving learning is a human learning technique where a learner inte...

Hao Ban, et al. ∙

research

∙ 03/11/2021

Learning by Teaching, with Application to Neural Architecture Search

In human learning, an effective skill in improving learning outcomes is ...

Parth Sheth, et al. ∙

research

∙ 03/09/2021

Self-supervised Regularization for Text Classification

Text classification is a widely studied problem and has broad applicatio...

Meng Zhou, et al. ∙

research

∙ 12/28/2020

Learning by Ignoring

Learning by ignoring, which identifies less important things and exclude...

Xingchen Zhao, et al. ∙

research

∙ 12/23/2020

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

Learning by self-explanation, where students explain a learned topic to ...

Ramtin Hosseini, et al. ∙

research

∙ 12/23/2020

Small-Group Learning, with Application to Neural Architecture Search

Small-group learning is a broadly used methodology in human learning and...

Xuefeng Du, et al. ∙

research

∙ 12/11/2020

DSRNA: Differentiable Search of Robust Neural Architectures

In deep learning applications, the architectures of deep neural networks...

Ramtin Hosseini, et al. ∙

research

∙ 12/09/2020

Skillearn: Machine Learning Inspired by Humans' Learning Skills

Humans, as the most powerful learners on the planet, have accumulated a ...

Pengtao Xie, et al. ∙

research

∙ 11/30/2020

Learning by Passing Tests, with Application to Neural Architecture Search

Learning through tests is a broadly used methodology in human learning a...

Xuefeng Du, et al. ∙

research

∙ 10/07/2020

Discriminative Cross-Modal Data Augmentation for Medical Imaging Applications

While deep learning methods have shown great success in medical image an...

Yue Yang, et al. ∙

research

∙ 09/16/2020

TreeGAN: Incorporating Class Hierarchy into Image Generation

Conditional image generation (CIG) is a widely studied problem in comput...

Ruisi Zhang, et al. ∙

research

∙ 09/13/2020

Contrastive Self-supervised Learning for Graph Classification

Graph classification is a widely studied problem and has broad applicati...

Jiaqi Zeng, et al. ∙

research

∙ 06/17/2020

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

To effectively train medical students to become qualified radiologists, ...

Xingyi Yang, et al. ∙

research

∙ 06/16/2020

Differentially-private Federated Neural Architecture Search

Neural architecture search, which aims to automatically search for archi...

Ishika Singh, et al. ∙

research

∙ 05/16/2020

CERT: Contrastive Self-supervised Learning for Language Understanding

Pretrained language models such as BERT, GPT have shown great effectiven...

Hongchao Fang, et al. ∙

research

∙ 05/11/2020

On the Generation of Medical Dialogues for COVID-19

Under the pandemic of COVID-19, people experiencing COVID19-related symp...

Wenmian Yang, et al. ∙

research

∙ 04/07/2020

MedDialog: A Large-scale Medical Dialogue Dataset

Medical dialogue systems are promising in assisting in telemedicine to i...

Shu Chen, et al. ∙

research

∙ 04/04/2020

Identifying Radiological Findings Related to COVID-19 from Medical Literature

Coronavirus disease 2019 (COVID-19) has infected more than one million i...

Yuxiao Liang, et al. ∙

research

∙ 03/30/2020

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

CT scans are promising in providing accurate, fast, and cheap screening ...

Jinyu Zhao, et al. ∙

research

∙ 03/07/2020

PathVQA: 30000+ Questions for Medical Visual Question Answering

Is it possible to develop an "AI Pathologist" to pass the board-certifie...

Xuehai He, et al. ∙

research

∙ 09/28/2019

Generalized Zero-shot ICD Coding

The International Classification of Diseases (ICD) is a list of classifi...

Congzheng Song, et al. ∙

research

∙ 05/28/2019

Adversarial Domain Adaptation Being Aware of Class Relationships

Adversarial training is a useful approach to promote the learning of tra...

Zeya Wang, et al. ∙

research

∙ 02/19/2019

Explaining a black-box using Deep Variational Information Bottleneck Approach

Briefness and comprehensiveness are necessary in order to give a lot of ...

Seojin Bang, et al. ∙

research

∙ 11/20/2018

Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records

Extractive summarization is very useful for physicians to better manage ...

Xiangan Liu, et al. ∙

research

∙ 11/19/2018

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

We study the problem of alleviating the instability issue in the GAN tra...

Hongyang Zhang, et al. ∙

research

∙ 10/31/2018

Multimodal Machine Learning for Automated ICD Coding

This study presents a multimodal machine learning model to predict ICD-1...

Keyang Xu, et al. ∙

research

∙ 08/05/2018

Missing Value Imputation Based on Deep Generative Models

Missing values widely exist in many real-world datasets, which hinders t...

Hongbao Zhang, et al. ∙

research

∙ 02/16/2018

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

Distance metric learning (DML), which learns a distance metric from labe...

Pengtao Xie, et al. ∙

research

∙ 12/06/2017

Convolutional Neural Networks for Medical Diagnosis from Admission Notes

Objective Develop an automatic diagnostic system which only uses textual...

Christy Li, et al. ∙

research

∙ 11/25/2017

Learning Less-Overlapping Representations

In representation learning (RL), how to make the learned representations...

Pengtao Xie, et al. ∙

research

∙ 11/25/2017

Stacked Kernel Network

Kernel methods are powerful tools to capture nonlinear patterns behind d...

Shuai Zhang, et al. ∙

research

∙ 11/23/2017

Diversity-Promoting Bayesian Learning of Latent Variable Models

To address three important issues involved in latent variable models (LV...

Pengtao Xie, et al. ∙

research

∙ 11/22/2017

On the Automatic Generation of Medical Imaging Reports

Medical imaging is widely used in clinical practice for diagnosis and tr...

Baoyu Jing, et al. ∙

research

∙ 11/21/2017

Effective Use of Bidirectional Language Modeling for Medical Named Entity Recognition

Biomedical named entity recognition (NER) is a fundamental task in text ...

Devendra Singh Sachan, et al. ∙

research

∙ 11/12/2017

Medical Diagnosis From Laboratory Tests by Combining Generative and Discriminative Learning

A primary goal of computational phenotype research is to conduct medical...

Shiyue Zhang, et al. ∙

research

∙ 11/11/2017

Towards Automated ICD Coding Using Deep Learning

International Classification of Diseases(ICD) is an authoritative health...

Haoran Shi, et al. ∙

research

∙ 11/04/2017

Predicting Discharge Medications at Admission Time Based on Deep Learning

Predicting discharge medications right after a patient being admitted is...

Yuan Yang, et al. ∙

research

∙ 06/11/2017

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...

Hao Zhang, et al. ∙

research

∙ 12/31/2015

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...

Eric P Xing, et al. ∙

research

∙ 12/23/2015

Latent Variable Modeling with Diversity-Inducing Mutual Angular Regularization

Latent Variable Models (LVMs) are a large family of machine learning mod...

Pengtao Xie, et al. ∙

research

∙ 12/19/2015

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

Deep learning (DL) has achieved notable successes in many machine learni...

Hao Zhang, et al. ∙

research

∙ 12/09/2015

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

We propose a distributed approach to train deep neural networks (DNNs), ...

Abhimanu Kumar, et al. ∙

research

∙ 12/19/2014

Cauchy Principal Component Analysis

Principal Component Analysis (PCA) has wide applications in machine lear...

Pengtao Xie, et al. ∙

research

∙ 12/18/2014

Crypto-Nets: Neural Networks over Encrypted Data

The problem we address is the following: how can a user employ a predict...

Pengtao Xie, et al. ∙

Pengtao Xie

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