Search Results for author: Sai Wu

Found 14 papers, 8 papers with code

ModelGiF: Gradient Fields for Model Functional Distance

1 code implementation ICCV 2023 Jie Song, Zhengqi Xu, Sai Wu, Gang Chen, Mingli Song

The last decade has witnessed the success of deep learning and the surge of publicly released trained models, which necessitates the quantification of the model functional distance for various purposes.

CT-BERT: Learning Better Tabular Representations Through Cross-Table Pre-training

no code implementations10 Jul 2023 Chao Ye, Guoshan Lu, Haobo Wang, Liyao Li, Sai Wu, Gang Chen, Junbo Zhao

Tabular data -- also known as structured data -- is one of the most common data forms in existence, thanks to the stable development and scaled deployment of database systems in the last few decades.

Contrastive Learning

Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility

1 code implementation15 May 2023 Wentao Ye, Mingfeng Ou, Tianyi Li, Yipeng chen, Xuetao Ma, Yifan Yanggong, Sai Wu, Jie Fu, Gang Chen, Haobo Wang, Junbo Zhao

With most of the related literature in the era of LLM uncharted, we propose an automated workflow that copes with an upscaled number of queries/responses.


Controllable Textual Inversion for Personalized Text-to-Image Generation

1 code implementation11 Apr 2023 Jianan Yang, Haobo Wang, YanMing Zhang, Ruixuan Xiao, Sai Wu, Gang Chen, Junbo Zhao

The recent large-scale generative modeling has attained unprecedented performance especially in producing high-fidelity images driven by text prompts.

Active Learning

Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting

1 code implementation13 Feb 2023 Yuchen Liu, Chen Chen, Lingjuan Lyu, Fangzhao Wu, Sai Wu, Gang Chen

In order to address this issue, we propose GAS, a \shorten approach that can successfully adapt existing robust AGRs to non-IID settings.

Federated Learning

Comparison Knowledge Translation for Generalizable Image Classification

1 code implementation7 May 2022 Zunlei Feng, Tian Qiu, Sai Wu, Xiaotuan Jin, Zengliang He, Mingli Song, Huiqiong Wang

In this paper, we attempt to build a generalizable framework that emulates the humans' recognition mechanism in the image classification task, hoping to improve the classification performance on unseen categories with the support of annotations of other categories.

Classification Image Classification +1

Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis

1 code implementation Findings (ACL) 2022 Yiming Zhang, Min Zhang, Sai Wu, Junbo Zhao

The aspect-based sentiment analysis (ABSA) is a fine-grained task that aims to determine the sentiment polarity towards targeted aspect terms occurring in the sentence.

Aspect-Based Sentiment Analysis (ABSA) Multi-Task Learning +1

Joining datasets via data augmentation in the label space for neural networks

no code implementations17 Jun 2021 Jake Zhao, Mingfeng Ou, Linji Xue, Yunkai Cui, Sai Wu, Gang Chen

Most, if not all, modern deep learning systems restrict themselves to a single dataset for neural network training and inference.

Data Augmentation text-classification +1

A critical look at the current train/test split in machine learning

no code implementations8 Jun 2021 Jimin Tan, Jianan Yang, Sai Wu, Gang Chen, Jake Zhao

The establishment of these split protocols are based on two assumptions: (i)-fixing the dataset to be eternally static so we could evaluate different machine learning algorithms or models; (ii)-there is a complete set of annotated data available to researchers or industrial practitioners.

Active Learning Benchmarking +2

Effective Slot Filling via Weakly-Supervised Dual-Model Learning

1 code implementation AAAI 2021 Jue Wang, Ke Chen, Lidan Shou, Sai Wu, Gang Chen

By using some particular weakly-labeled data, namely the plain phrases included in sentences, we propose a weaklysupervised slot filling approach.

slot-filling Slot Filling +1

LINDT: Tackling Negative Federated Learning with Local Adaptation

no code implementations23 Nov 2020 Hong Lin, Lidan Shou, Ke Chen, Gang Chen, Sai Wu

On occasion of NFL recovery, the framework makes adaptation to the federated model on each client's local data by learning a Layer-wise Intertwined Dual-model.

Federated Learning

BERT-JAM: Boosting BERT-Enhanced Neural Machine Translation with Joint Attention

no code implementations9 Nov 2020 Zhebin Zhang, Sai Wu, Dawei Jiang, Gang Chen

In this work, we propose a novel BERT-enhanced NMT model called BERT-JAM which improves upon existing models from two aspects: 1) BERT-JAM uses joint-attention modules to allow the encoder/decoder layers to dynamically allocate attention between different representations, and 2) BERT-JAM allows the encoder/decoder layers to make use of BERT's intermediate representations by composing them using a gated linear unit (GLU).

Machine Translation NMT +1

Semi-Supervised Few-Shot Learning for Dual Question-Answer Extraction

no code implementations8 Apr 2019 Jue Wang, Ke Chen, Lidan Shou, Sai Wu, Sharad Mehrotra

In this paper, we redefine the problem as question-answer extraction, and present SAMIE: Self-Asking Model for Information Ixtraction, a semi-supervised model which dually learns to ask and to answer questions by itself.

Clustering Few-Shot Learning

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