Search Results for author: Ying WEI

Found 54 papers, 22 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

MetaTS: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision

no code implementations EMNLP 2021 Zheng Li, Danqing Zhang, Tianyu Cao, Ying WEI, Yiwei Song, Bing Yin

In this work, we explore multilingual sequence labeling with minimal supervision using a single unified model for multiple languages.


Benchmarking and Improving Compositional Generalization of Multi-aspect Controllable Text Generation

1 code implementation5 Apr 2024 Tianqi Zhong, Zhaoyi Li, Quan Wang, Linqi Song, Ying WEI, Defu Lian, Zhendong Mao

Compositional generalization, representing the model's ability to generate text with new attribute combinations obtained by recombining single attributes from the training data, is a crucial property for multi-aspect controllable text generation (MCTG) methods.

Attribute Benchmarking +2

Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts

1 code implementation13 Mar 2024 Shengzhuang Chen, Jihoon Tack, Yunqiao Yang, Yee Whye Teh, Jonathan Richard Schwarz, Ying WEI

Conventional wisdom suggests parameter-efficient fine-tuning of foundation models as the state-of-the-art method for transfer learning in vision, replacing the rich literature of alternatives such as meta-learning.

Domain Generalization Few-Shot Image Classification +2

MoPE-CLIP: Structured Pruning for Efficient Vision-Language Models with Module-wise Pruning Error Metric

no code implementations12 Mar 2024 Haokun Lin, Haoli Bai, Zhili Liu, Lu Hou, Muyi Sun, Linqi Song, Ying WEI, Zhenan Sun

We find that directly using smaller pre-trained models and applying magnitude-based pruning on CLIP models leads to inflexibility and inferior performance.

Understanding and Patching Compositional Reasoning in LLMs

no code implementations22 Feb 2024 Zhaoyi Li, Gangwei Jiang, Hong Xie, Linqi Song, Defu Lian, Ying WEI

LLMs have marked a revolutonary shift, yet they falter when faced with compositional reasoning tasks.

From Words to Molecules: A Survey of Large Language Models in Chemistry

no code implementations2 Feb 2024 Chang Liao, Yemin Yu, Yu Mei, Ying WEI

After that, we explore the diverse applications of LLMs in chemistry, including novel paradigms for their application in chemistry tasks.

Continual Learning

Understanding the Multi-modal Prompts of the Pre-trained Vision-Language Model

no code implementations18 Dec 2023 Shuailei Ma, Chen-Wei Xie, Ying WEI, Siyang Sun, Jiaqi Fan, Xiaoyi Bao, Yuxin Guo, Yun Zheng

In this paper, we conduct a direct analysis of the multi-modal prompts by asking the following questions: $(i)$ How do the learned multi-modal prompts improve the recognition performance?

Language Modelling

RetroOOD: Understanding Out-of-Distribution Generalization in Retrosynthesis Prediction

no code implementations18 Dec 2023 Yemin Yu, Luotian Yuan, Ying WEI, Hanyu Gao, Xinhai Ye, Zhihua Wang, Fei Wu

Machine learning-assisted retrosynthesis prediction models have been gaining widespread adoption, though their performances oftentimes degrade significantly when deployed in real-world applications embracing out-of-distribution (OOD) molecules or reactions.

Out-of-Distribution Generalization Retrosynthesis

SKDF: A Simple Knowledge Distillation Framework for Distilling Open-Vocabulary Knowledge to Open-world Object Detector

1 code implementation14 Dec 2023 Shuailei Ma, Yuefeng Wang, Ying WEI, Jiaqi Fan, Enming Zhang, Xinyu Sun, Peihao Chen

Ablation experiments demonstrate that both of them are effective in mitigating the impact of open-world knowledge distillation on the learning of known objects.

Knowledge Distillation Object +3

Concept-wise Fine-tuning Matters in Preventing Negative Transfer

no code implementations ICCV 2023 Yunqiao Yang, Long-Kai Huang, Ying WEI

A multitude of prevalent pre-trained models mark a major milestone in the development of artificial intelligence, while fine-tuning has been a common practice that enables pretrained models to figure prominently in a wide array of target datasets.

High-fidelity 3D Reconstruction of Plants using Neural Radiance Field

no code implementations7 Nov 2023 Kewei Hu, Ying WEI, Yaoqiang Pan, Hanwen Kang, Chao Chen

Recently, a promising development has emerged in the form of Neural Radiance Field (NeRF), a novel method that utilises neural density fields.

3D Reconstruction Plant Phenotyping

Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompt

1 code implementation19 Oct 2023 Gangwei Jiang, Caigao Jiang, Siqiao Xue, James Y. Zhang, Jun Zhou, Defu Lian, Ying WEI

In this work, we first investigate such anytime fine-tuning effectiveness of existing continual pre-training approaches, concluding with unanimously decreased performance on unseen domains.

Transfer Learning

Learning to Substitute Spans towards Improving Compositional Generalization

1 code implementation5 Jun 2023 Zhaoyi Li, Ying WEI, Defu Lian

Despite the rising prevalence of neural sequence models, recent empirical evidences suggest their deficiency in compositional generalization.

Data Augmentation Inductive Bias +1

Detecting the open-world objects with the help of the Brain

1 code implementation21 Mar 2023 Shuailei Ma, Yuefeng Wang, Ying WEI, Peihao Chen, Zhixiang Ye, Jiaqi Fan, Enming Zhang, Thomas H. Li

We propose leveraging the VL as the ``Brain'' of the open-world detector by simply generating unknown labels.

Object object-detection +1

FGAHOI: Fine-Grained Anchors for Human-Object Interaction Detection

1 code implementation8 Jan 2023 Shuailei Ma, Yuefeng Wang, Shanze Wang, Ying WEI

HSAM and TAM semantically align and merge the extracted features and query embeddings in the hierarchical spatial and task perspectives in turn.

Human-Object Interaction Detection Object +1

CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection

no code implementations CVPR 2023 Shuailei Ma, Yuefeng Wang, Jiaqi Fan, Ying WEI, Thomas H. Li, Hongli Liu, Fanbing Lv

Open-world object detection (OWOD), as a more general and challenging goal, requires the model trained from data on known objects to detect both known and unknown objects and incrementally learn to identify these unknown objects.

object-detection Open World Object Detection

Hybrid Censored Quantile Regression Forest to Assess the Heterogeneous Effects

no code implementations12 Dec 2022 Huichen Zhu, Yifei Sun, Ying WEI

We propose a variable importance decomposition to measure the impact of a variable on the treatment effect function.


Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clustering

no code implementations16 Nov 2022 Juan Zha, Zheng Li, Ying WEI, Yu Zhang

However, most prior works assume that all the tasks are sampled from a single data source, which cannot adapt to real-world scenarios where tasks are heterogeneous and lie in different distributions.

Clustering Few-Shot Text Classification +1

Wasserstein Distributional Learning

no code implementations12 Sep 2022 Chengliang Tang, Nathan Lenssen, Ying WEI, Tian Zheng

To overcome this fundamental issue, we propose Wasserstein Distributional Learning (WDL), a flexible density-on-scalar regression modeling framework that starts with the Wasserstein distance $W_2$ as a proper metric for the space of density outcomes.


Learning to generate imaginary tasks for improving generalization in meta-learning

no code implementations9 Jun 2022 Yichen Wu, Long-Kai Huang, Ying WEI

The success of meta-learning on existing benchmarks is predicated on the assumption that the distribution of meta-training tasks covers meta-testing tasks.

Image Classification Memorization +2

Discriminative-Region Attention and Orthogonal-View Generation Model for Vehicle Re-Identification

no code implementations28 Apr 2022 Huadong Li, Yuefeng Wang, Ying WEI, Lin Wang, Li Ge

Finally, the distance between vehicle appearances is presented by the discriminative region features and multi-view features together.

Attribute Management +1

Self-Supervised Text Erasing with Controllable Image Synthesis

no code implementations27 Apr 2022 Gangwei Jiang, Shiyao Wang, Tiezheng Ge, Yuning Jiang, Ying WEI, Defu Lian

The synthetic training images with erasure ground-truth are then fed to train a coarse-to-fine erasing network.

Image Generation

Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery

no code implementations NeurIPS 2021 Huaxiu Yao, Ying WEI, Long-Kai Huang, Ding Xue, Junzhou Huang, Zhenhui (Jessie) Li

More recently, there has been a surge of interest in employing machine learning approaches to expedite the drug discovery process where virtual screening for hit discovery and ADMET prediction for lead optimization play essential roles.

Drug Discovery Meta-Learning +1

Meta-learning with an Adaptive Task Scheduler

2 code implementations NeurIPS 2021 Huaxiu Yao, Yu Wang, Ying WEI, Peilin Zhao, Mehrdad Mahdavi, Defu Lian, Chelsea Finn

In ATS, for the first time, we design a neural scheduler to decide which meta-training tasks to use next by predicting the probability being sampled for each candidate task, and train the scheduler to optimize the generalization capacity of the meta-model to unseen tasks.

Drug Discovery Meta-Learning

Minimizing Memorization in Meta-learning: A Causal Perspective

no code implementations29 Sep 2021 Yinjie Jiang, Zhengyu Chen, Luotian Yuan, Ying WEI, Kun Kuang, Xinhai Ye, Zhihua Wang, Fei Wu

Meta-learning has emerged as a potent paradigm for quick learning of few-shot tasks, by leveraging the meta-knowledge learned from meta-training tasks.

Causal Inference Memorization +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 in it for transfer learning, 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

1 code implementation 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.

Multivariate Time Series Forecasting Probabilistic Time Series Forecasting +1

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.


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

3 code implementations 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 molecular representation +2

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.

BIG-bench Machine Learning Hyperparameter Optimization +1

Hierarchically Structured Meta-learning

1 code implementation13 May 2019 Huaxiu Yao, Ying WEI, Junzhou Huang, Zhenhui Li

In order to learn quickly with few samples, meta-learning utilizes prior knowledge learned from previous tasks.

Clustering Continual Learning +2

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 Sentence +2

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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