b'Pengtao Xie'

research

∙ 06/20/2023

LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching

Obtaining large pre-trained models that can be fine-tuned to new tasks w...

8 Duy M. H. Nguyen, et al. ∙

research

∙ 04/02/2023

Learning by Grouping: A Multilevel Optimization Framework for Improving Fairness in Classification without Losing Accuracy

The integration of machine learning models in various real-world applica...

0 Ramtin Hosseini, et al. ∙

research

∙ 02/18/2023

A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

The Pretrained Foundation Models (PFMs) are regarded as the foundation f...

0 Ce Zhou, et al. ∙

research

∙ 01/09/2023

Learning to compile smartly for program size reduction

Compiler optimization passes are an important tool for improving program...

1 Youwei Liang, et al. ∙

research

∙ 12/30/2022

DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world...

0 Hasan Md Tusfiqur, et al. ∙

research

∙ 12/04/2022

Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering

Collecting large-scale medical datasets with fully annotated samples for...

10 Duy M. H. Nguyen, et al. ∙

research

∙ 11/15/2022

Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems

Event detection in power systems aims to identify triggers and event typ...

0 Qian Li, et al. ∙

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...

9 Youwei Liang, et al. ∙

research

∙ 01/04/2022

Self-directed Machine Learning

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

0 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 ...

0 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...

0 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...

58 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...

0 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...

0 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 ...

0 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...

0 Meng Zhou, et al. ∙

research

∙ 12/28/2020

Learning by Ignoring

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

0 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 ...

0 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...

0 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...

0 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 ...

0 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...

0 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...

4 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...

41 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...

29 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, ...

20 Xingyi Yang, et al. ∙

research

∙ 06/16/2020

Differentially-private Federated Neural Architecture Search

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

0 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...

0 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...

0 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...

9 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...

0 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 ...

78 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...

0 Xuehai He, et al. ∙

research

∙ 09/28/2019

Generalized Zero-shot ICD Coding

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

0 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...

1 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 ...

8 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 ...

0 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...

8 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...

4 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...

14 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...

0 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...

0 Christy Li, et al. ∙

research

∙ 11/25/2017

Learning Less-Overlapping Representations

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

0 Pengtao Xie, et al. ∙

research

∙ 11/25/2017

Stacked Kernel Network

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

0 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...

0 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...

0 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 ...

0 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...

0 Shiyue Zhang, et al. ∙

research

∙ 11/11/2017

Towards Automated ICD Coding Using Deep Learning

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

0 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...

0 Yuan Yang, et al. ∙

Pengtao Xie

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