no code implementations • 17 Jul 2019 • Lanyu Shang, Daniel Zhang, Michael Wang, Shuyue Lai, Dong Wang
Current clickbait detection solutions that mainly focus on analyzing the text of the title, the image of the thumbnail, or the content of the video are shown to be suboptimal in detecting the online clickbait videos.
no code implementations • 21 Jun 2021 • Lanyu Shang, Yang Zhang, Yuheng Zha, Yingxi Chen, Christina Youn, Dong Wang
To address the above challenges, we develop a deep learning based Analogy-aware Offensive Meme Detection (AOMD) framework to learn the implicit analogy from the multi-modal contents of the meme and effectively detect offensive analogy memes.
no code implementations • 29 Mar 2022 • Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang
Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time.
1 code implementation • 19 Jul 2022 • Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang
Additionally, we design an adversarial training method tailored for sequential recommender systems.
2 code implementations • 20 Aug 2022 • Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang
However, early misinformation often demonstrates both conditional and label shifts against existing misinformation data (e. g., class imbalance in COVID-19 datasets), rendering such methods less effective for detecting early misinformation.
1 code implementation • COLING 2022 • Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang
In this work, we investigate the potential benefits of question classification for QA domain adaptation.
no code implementations • 3 Oct 2022 • Huimin Zeng, Zhenrui Yue, Yang Zhang, Ziyi Kou, Lanyu Shang, Dong Wang
In many applications with real-world consequences, it is crucial to develop reliable uncertainty estimation for the predictions made by the AI decision systems.
no code implementations • 6 Oct 2022 • Huimin Zeng, Zhenrui Yue, Ziyi Kou, Lanyu Shang, Yang Zhang, Dong Wang
Moreover, we leverage the power of domain adversarial examples to establish an intermediate domain mixup, where the latent representations of the input text from both domains could be mixed during the training process.
1 code implementation • 22 May 2023 • Zhenrui Yue, Huimin Zeng, Yang Zhang, Lanyu Shang, Dong Wang
As such, MetaAdapt can learn how to adapt the misinformation detection model and exploit the source data for improved performance in the target domain.
no code implementations • 22 Mar 2024 • Zhenrui Yue, Huimin Zeng, Yimeng Lu, Lanyu Shang, Yang Zhang, Dong Wang
The proliferation of online misinformation has posed significant threats to public interest.