2 code implementations • 25 Aug 2023 • Yonghao Song, Bingchuan Liu, Xiang Li, Nanlin Shi, Yijun Wang, Xiaorong Gao
This paper presents a self-supervised framework to demonstrate the feasibility of learning image representations from EEG signals, particularly for object recognition.
no code implementations • 13 Sep 2021 • Lei Shen, Haolan Zhan, Xin Shen, Yonghao Song, Xiaofang Zhao
Specifically, we obtain a group of images (PVIs) for each post based on a pre-trained word-image mapping model.
no code implementations • 26 Jun 2021 • Xu Yuan, Hongshen Chen, Yonghao Song, Xiaofang Zhao, Zhuoye Ding, Zhen He, Bo Long
In this paper, we propose a model, SSI, to improve sequential recommendation consistency with Self-Supervised Imitation.
3 code implementations • 11 Jun 2021 • Yonghao Song, Xueyu Jia, Lie Yang, Longhan Xie
As far as we know, it is the first time that a detailed and complete method based on the transformer idea has been proposed in this field.
no code implementations • 8 Feb 2021 • Yonghao Song, Lie Yang, Xueyu Jia, Longhan Xie
The cross-subject application of EEG-based brain-computer interface (BCI) has always been limited by large individual difference and complex characteristics that are difficult to perceive.
2 code implementations • Findings of the Association for Computational Linguistics 2020 • Hengyi Cai, Hongshen Chen, Yonghao Song, Zhuoye Ding, Yongjun Bao, Weipeng Yan, Xiaofang Zhao
Neural dialogue response generation has gained much popularity in recent years.
no code implementations • ACL 2020 • Hengyi Cai, Hongshen Chen, Yonghao Song, Cheng Zhang, Xiaofang Zhao, Dawei Yin
In this paper, we propose a data manipulation framework to proactively reshape the data distribution towards reliable samples by augmenting and highlighting effective learning samples as well as reducing the effect of inefficient samples simultaneously.
1 code implementation • 2 Mar 2020 • Hengyi Cai, Hongshen Chen, Cheng Zhang, Yonghao Song, Xiaofang Zhao, Yangxi Li, Dongsheng Duan, Dawei Yin
Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses.
1 code implementation • IJCNLP 2019 • Hengyi Cai, Hongshen Chen, Cheng Zhang, Yonghao Song, Xiaofang Zhao, Dawei Yin
For each conversation, the model generates parameters of the encoder-decoder by referring to the input context.
no code implementations • 2 May 2018 • Hengyi Cai, Xingguang Ji, Yonghao Song, Yan Jin, Yang Zhang, Mairgup Mansur, Xiaofang Zhao
In contrast to previous work, KNPTC is able to integrate explicit knowledge into NMT for pinyin typo correction, and is able to learn to correct a variety of typos without the guidance of manually selected constraints or languagespecific features.