Search Results for author: Ying WEI

Found 27 papers, 12 papers with code

Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages

no code implementations EMNLP 2020 Zheng Li, Mukul Kumar, William Headden, Bing Yin, Ying WEI, Yu Zhang, Qiang Yang

Recent emergence of multilingual pre-training language model (mPLM) has enabled breakthroughs on various downstream cross-lingual transfer (CLT) tasks.

Cross-Lingual Transfer Graph Learning +1

Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation

no code implementations8 Sep 2021 Geng-Xin Xu, Chen Liu, Jun Liu, Zhongxiang Ding, Feng Shi, Man Guo, Wei Zhao, Xiaoming Li, Ying WEI, Yaozong Gao, Chuan-Xian Ren, Dinggang Shen

Particularly, we propose a domain translator and align the heterogeneous data to the estimated class prototypes (i. e., class centers) in a hyper-sphere manifold.

Computed Tomography (CT) Domain Adaptation +1

Frustratingly Easy Transferability Estimation

no code implementations17 Jun 2021 Long-Kai Huang, Ying WEI, Yu Rong, Qiang Yang, Junzhou Huang

Transferability estimation has been an essential tool in selecting a pre-trained model and the layers of it to transfer, so as to maximize the performance on a target task and prevent negative transfer.

Mutual Information Estimation Transfer Learning

Adversarial Sparse Transformer for Time Series Forecasting

no code implementations NeurIPS 2020 Sifan Wu, Xi Xiao, Qianggang Ding, Peilin Zhao, Ying WEI, Junzhou Huang

Specifically, AST adopts a Sparse Transformer as the generator to learn a sparse attention map for time series forecasting, and uses a discriminator to improve the prediction performance from sequence level.

Time Series Time Series Forecasting

Improving Generalization in Meta-learning via Task Augmentation

1 code implementation26 Jul 2020 Huaxiu Yao, Long-Kai Huang, Linjun Zhang, Ying WEI, Li Tian, James Zou, Junzhou Huang, Zhenhui Li

Moreover, both MetaMix and Channel Shuffle outperform state-of-the-art results by a large margin across many datasets and are compatible with existing meta-learning algorithms.

Meta-Learning

Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

no code implementations4 Jul 2020 Yue Sun, Kun Gao, Zhengwang Wu, Zhihao Lei, Ying WEI, Jun Ma, Xiaoping Yang, Xue Feng, Li Zhao, Trung Le Phan, Jitae Shin, Tao Zhong, Yu Zhang, Lequan Yu, Caizi Li, Ramesh Basnet, M. Omair Ahmad, M. N. S. Swamy, Wenao Ma, Qi Dou, Toan Duc Bui, Camilo Bermudez Noguera, Bennett Landman, Ian H. Gotlib, Kathryn L. Humphreys, Sarah Shultz, Longchuan Li, Sijie Niu, Weili Lin, Valerie Jewells, Gang Li, Dinggang Shen, Li Wang

Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.

Brain Segmentation

Self-Supervised Graph Transformer on Large-Scale Molecular Data

1 code implementation NeurIPS 2020 Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying WEI, Wenbing Huang, Junzhou Huang

We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning.

Molecular Property Prediction Representation Learning

Hypergraph Learning for Identification of COVID-19 with CT Imaging

no code implementations7 May 2020 Donglin Di, Feng Shi, Fuhua Yan, Liming Xia, Zhanhao Mo, Zhongxiang Ding, Fei Shan, Shengrui Li, Ying WEI, Ying Shao, Miaofei Han, Yaozong Gao, He Sui, Yue Gao, Dinggang Shen

The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features.

COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19

1 code implementation30 Apr 2020 Yifan Zhang, Shuaicheng Niu, Zhen Qiu, Ying WEI, Peilin Zhao, Jianhua Yao, Junzhou Huang, Qingyao Wu, Mingkui Tan

There are two main challenges: 1) the discrepancy of data distributions between domains; 2) the task difference between the diagnosis of typical pneumonia and COVID-19.

COVID-19 Diagnosis Domain Adaptation

Fisher Deep Domain Adaptation

1 code implementation12 Mar 2020 Yinghua Zhang, Yu Zhang, Ying WEI, Kun Bai, Yangqiu Song, Qiang Yang

Though the learned representations are separable in the source domain, they usually have a large variance and samples with different class labels tend to overlap in the target domain, which yields suboptimal adaptation performance.

Domain Adaptation

Infant brain MRI segmentation with dilated convolution pyramid downsampling and self-attention

no code implementations29 Dec 2019 Zhihao Lei, Lin Qi, Ying WEI, Yunlong Zhou

In this paper, we propose a dual aggregation network to adaptively aggregate different information in infant brain MRI segmentation.

Infant Brain Mri Segmentation MRI segmentation

Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis

1 code implementation17 Nov 2019 Yifan Zhang, Ying WEI, Peilin Zhao, Shuaicheng Niu, Qingyao Wu, Mingkui Tan, Junzhou Huang

In this paper, we seek to exploit rich labeled data from relevant domains to help the learning in the target task with unsupervised domain adaptation (UDA).

Unsupervised Domain Adaptation

Transferable Neural Processes for Hyperparameter Optimization

no code implementations7 Sep 2019 Ying Wei, Peilin Zhao, Huaxiu Yao, Junzhou Huang

Automated machine learning aims to automate the whole process of machine learning, including model configuration.

Hyperparameter Optimization Transfer Learning

Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification

1 code implementation AAAI 2019 2018 Zheng Li, Ying WEI, Yu Zhang, Xiang Zhang, Xin Li, Qiang Yang

Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term (AT).

General Classification Sentiment Analysis

Transfer Learning via Learning to Transfer

no code implementations ICML 2018 Ying WEI, Yu Zhang, Junzhou Huang, Qiang Yang

In transfer learning, what and how to transfer are two primary issues to be addressed, as different transfer learning algorithms applied between a source and a target domain result in different knowledge transferred and thereby the performance improvement in the target domain.

Transfer Learning

Learning to Multitask

no code implementations NeurIPS 2018 Yu Zhang, Ying WEI, Qiang Yang

Based on such training set, L2MT first uses a proposed layerwise graph neural network to learn task embeddings for all the tasks in a multitask problem and then learns an estimation function to estimate the relative test error based on task embeddings and the representation of the multitask model based on a unified formulation.

Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification

1 code implementation Thirty-Second AAAI Conference on Artificial Intelligence 2018 Zheng Li, Ying WEI, Yu Zhang, Qiang Yang

Existing cross-domain sentiment classification meth- ods cannot automatically capture non-pivots, i. e., the domain- specific sentiment words, and pivots, i. e., the domain-shared sentiment words, simultaneously.

Classification Cross-Domain Text Classification +4

Learning to Transfer

no code implementations18 Aug 2017 Ying Wei, Yu Zhang, Qiang Yang

We establish the L2T framework in two stages: 1) we first learn a reflection function encrypting transfer learning skills from experiences; and 2) we infer what and how to transfer for a newly arrived pair of domains by optimizing the reflection function.

Transfer Learning

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