Search Results for author: Xianchao Zhang

Found 11 papers, 1 papers with code

HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text

1 code implementation NeurIPS 2023 Han Liu, Zhi Xu, Xiaotong Zhang, Feng Zhang, Fenglong Ma, Hongyang Chen, Hong Yu, Xianchao Zhang

Black-box hard-label adversarial attack on text is a practical and challenging task, as the text data space is inherently discrete and non-differentiable, and only the predicted label is accessible.

Adversarial Attack Hard-label Attack +5

Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors

no code implementations29 Oct 2023 Han Liu, Xingshuo Huang, Xiaotong Zhang, Qimai Li, Fenglong Ma, Wei Wang, Hongyang Chen, Hong Yu, Xianchao Zhang

Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model prediction.

Adversarial Attack

Boosting Few-Shot Text Classification via Distribution Estimation

no code implementations26 Mar 2023 Han Liu, Feng Zhang, Xiaotong Zhang, Siyang Zhao, Fenglong Ma, Xiao-Ming Wu, Hongyang Chen, Hong Yu, Xianchao Zhang

Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain.

Few-Shot Image Classification Few-Shot Text Classification +1

Infomaxformer: Maximum Entropy Transformer for Long Time-Series Forecasting Problem

no code implementations4 Jan 2023 Peiwang Tang, Xianchao Zhang

The Transformer architecture yields state-of-the-art results in many tasks such as natural language processing (NLP) and computer vision (CV), since the ability to efficiently capture the precise long-range dependency coupling between input sequences.

Time Series Time Series Forecasting

MTSMAE: Masked Autoencoders for Multivariate Time-Series Forecasting

no code implementations4 Oct 2022 Peiwang Tang, Xianchao Zhang

Large-scale self-supervised pre-training Transformer architecture have significantly boosted the performance for various tasks in natural language processing (NLP) and computer vision (CV).

Multivariate Time Series Forecasting Self-Supervised Learning +1

Features Fusion Framework for Multimodal Irregular Time-series Events

no code implementations5 Sep 2022 Peiwang Tang, Xianchao Zhang

Firstly, the complex features are extracted according to the irregular patterns of different events.

Irregular Time Series Time Series +1

Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection

no code implementations14 Jun 2022 Han Liu, Feng Zhang, Xiaotong Zhang, Siyang Zhao, Junjie Sun, Hong Yu, Xianchao Zhang

Multi-label aspect category detection allows a given review sentence to contain multiple aspect categories, which is shown to be more practical in sentiment analysis and attracting increasing attention.

Aspect Category Detection Contrastive Learning +2

A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism

no code implementations5 Jun 2022 Han Liu, Siyang Zhao, Xiaotong Zhang, Feng Zhang, Junjie Sun, Hong Yu, Xianchao Zhang

Zero-shot intent classification is a vital and challenging task in dialogue systems, which aims to deal with numerous fast-emerging unacquainted intents without annotated training data.

Classification intent-classification +4

Posterior Promoted GAN With Distribution Discriminator for Unsupervised Image Synthesis

no code implementations CVPR 2021 Xianchao Zhang, Ziyang Cheng, Xiaotong Zhang, Han Liu

In this paper, we propose a novel variant of GAN, Posterior Promoted GAN (P2GAN), which promotes generator with the real information in the posterior distribution produced by discriminator.

Image Generation

Clustering Uncertain Data via Representative Possible Worlds with Consistency Learning

no code implementations27 Sep 2019 Han Liu, Xianchao Zhang, Xiaotong Zhang, Qimai Li, Xiao-Ming Wu

However, there are two issues in existing possible world based algorithms: (1) They rely on all the possible worlds and treat them equally, but some marginal possible worlds may cause negative effects.

Clustering

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