1 code implementation • EMNLP 2021 • Haoran Li, Song Xu, Peng Yuan, Yujia Wang, Youzheng Wu, Xiaodong He, BoWen Zhou
It thereby takes advantage of prior copying distributions and, at each time step, explicitly encourages the model to copy the input word that is relevant to the previously copied one.
Ranked #11 on Abstractive Text Summarization on CNN / Daily Mail (using extra training data)
no code implementations • 29 Mar 2024 • Yongqi Tong, Dawei Li, Sizhe Wang, Yujia Wang, Fei Teng, Jingbo Shang
We conduct a series of experiments to prove LLMs can obtain benefits from mistakes in both directions.
no code implementations • 22 Dec 2023 • Tiejin Chen, Yuanpu Cao, Yujia Wang, Cho-Jui Hsieh, Jinghui Chen
Specifically, FedPTR allows local clients or the server to optimize an auxiliary (synthetic) dataset that mimics the learning dynamics of the recent model update and utilizes it to project the next-step model trajectory for local training regularization.
no code implementations • 26 Oct 2023 • Zi Lin, Zihan Wang, Yongqi Tong, Yangkun Wang, Yuxin Guo, Yujia Wang, Jingbo Shang
This benchmark contains the rich, nuanced phenomena that can be tricky for current toxicity detection models to identify, revealing a significant domain difference compared to social media content.
1 code implementation • 5 May 2022 • Yujia Wang, Lu Lin, Jinghui Chen
We show that in the nonconvex stochastic optimization setting, our proposed FedCAMS achieves the same convergence rate of $O(\frac{1}{\sqrt{TKm}})$ as its non-compressed counterparts.
no code implementations • 1 Nov 2021 • Yujia Wang, Lu Lin, Jinghui Chen
We prove that the proposed communication-efficient distributed adaptive gradient method converges to the first-order stationary point with the same iteration complexity as uncompressed vanilla AMSGrad in the stochastic nonconvex optimization setting.
1 code implementation • Findings (EMNLP) 2021 • Song Xu, Haoran Li, Peng Yuan, Yujia Wang, Youzheng Wu, Xiaodong He, Ying Liu, BoWen Zhou
K-PLUG achieves new state-of-the-art results on a suite of domain-specific NLP tasks, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue, significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks.
1 code implementation • 10 Feb 2021 • Xiangyu Zhao, Peng Zhang, Fan Song, Guangda Fan, Yangyang Sun, Yujia Wang, Zheyuan Tian, Luqi Zhang, Guanglei Zhang
In this paper we propose a dilated dual attention U-Net (D2A U-Net) for COVID-19 lesion segmentation in CT slices based on dilated convolution and a novel dual attention mechanism to address the issues above.
1 code implementation • 1 Jan 2021 • Song Xu, Haoran Li, Peng Yuan, Yujia Wang, Youzheng Wu, Xiaodong He, Ying Liu, BoWen Zhou
K-PLUG achieves new state-of-the-art results on a suite of domain-specific NLP tasks, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue, significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks.
no code implementations • MIDL 2019 • Yichi Zhang, Lin Yuan, Yujia Wang, Jicong Zhang
Accurate segmentation of spine Magnetic Resonance Imaging (MRI) is highly demanded in morphological research, quantitative analysis, and diseases identification, such as spinal canal stenosis, disc herniation and degeneration.
no code implementations • 26 Nov 2018 • Qihao Liu, Yujia Wang, Xiaofeng Liu
To balance exploration and exploitation, the Novelty Search (NS) is employed in every chief agent to encourage policies with high novelty while maximizing per-episode performance.
no code implementations • 27 Sep 2018 • Yining Lang, Wei Liang, Yujia Wang, Lap-Fai Yu
In this paper, we propose a novel approach to synthesize 3D faces based on personality impression for creating virtual characters.
Graphics