no code implementations • EMNLP (IWSLT) 2019 • Yuchen Yan, Dekai Wu, Serkan Kumyol
We introduce (1) a novel neural network structure for bilingual modeling of sentence pairs that allows efficient capturing of bilingual relationship via biconstituent composition, (2) the concept of neural network biparsing, which applies to not only machine translation (MT) but also to a variety of other bilingual research areas, and (3) the concept of a biparsing-backpropagation training loop, which we hypothesize that can efficiently learn complex biparse tree patterns.
no code implementations • 26 Feb 2024 • Yihang Zhou, Qingqing Long, Yuchen Yan, Xiao Luo, Zeyu Dong, Xuezhi Wang, Zhen Meng, Pengfei Wang, Yuanchun Zhou
Zero-shot hashing (ZSH) has shown excellent success owing to its efficiency and generalization in large-scale retrieval scenarios.
no code implementations • 22 Feb 2024 • Chang Zong, Yuchen Yan, Weiming Lu, Jian Shao, Eliot Huang, Heng Chang, Yueting Zhuang
We evaluated the performance of our framework using three benchmark datasets, and the results show that our framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks, yielding F1 scores of 11. 8% and 20. 7%, respectively.
no code implementations • 21 Feb 2024 • Yuchen Yan, Peiyan Zhang, Zheng Fang, Qingqing Long
Based on the insight of graph pre-training, we propose to bridge the graph signal gap and the graph structure gap with learnable prompts in the spectral space.
no code implementations • 13 Dec 2023 • Zhe Xu, Menghai Pan, Yuzhong Chen, Huiyuan Chen, Yuchen Yan, Mahashweta Das, Hanghang Tong
Based on the self-attention module, our proposed invariant graph Transformer (IGT) can achieve fine-grained, more specifically, node-level and virtual node-level intervention.
no code implementations • 27 Nov 2023 • Hanjie Zhao, Jinge Xie, Yuchen Yan, Yuxiang Jia, Yawen Ye, Hongying Zan
Entities like person, location, organization are important for literary text analysis.
no code implementations • 16 Nov 2023 • Wentao He, Yuchen Yan, Jianfeng Ren, Ruibin Bai, Xudong Jiang
Deep neural networks have been applied to audio spectrograms for respiratory sound classification.
no code implementations • 28 Aug 2023 • Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Senzhang Wang, Xing Xie, Sunghun Kim
Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative filtering (CF) through the modeling of user-item interaction graphs.
1 code implementation • 19 Jul 2023 • Ved Piyush, Yuchen Yan, Yuzhen Zhou, Yanbin Yin, Souparno Ghosh
Deep Learners (DLs) are the state-of-art predictive mechanism with applications in many fields requiring complex high dimensional data processing.
no code implementations • 13 Jun 2023 • Ruijie Wang, Baoyu Li, Yichen Lu, Dachun Sun, Jinning Li, Yuchen Yan, Shengzhong Liu, Hanghang Tong, Tarek F. Abdelzaher
State-of-the-art methods fall short in the speculative reasoning ability, as they assume the correctness of a fact is solely determined by its presence in KG, making them vulnerable to false negative/positive issues.
no code implementations • 29 May 2023 • Dingsu Wang, Yuchen Yan, Ruizhong Qiu, Yada Zhu, Kaiyu Guan, Andrew J Margenot, Hanghang Tong
First, we define the problem of imputation over NTS which contains missing values in both node time series features and graph structures.
1 code implementation • 23 May 2023 • Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Senzhang Wang, Xing Xie, Guojie Song, Sunghun Kim
Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs.
1 code implementation • 5 May 2023 • Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He
In recent literature, a series of neural bandit algorithms have been proposed to adapt to the non-linear reward function, combined with TS or UCB strategies for exploration.
no code implementations • 25 Jan 2023 • Baoyu Jing, Yuchen Yan, Kaize Ding, Chanyoung Park, Yada Zhu, Huan Liu, Hanghang Tong
Most recent bipartite graph SSL methods are based on contrastive learning which learns embeddings by discriminating positive and negative node pairs.
no code implementations • 31 May 2022 • Baoyu Jing, Yuchen Yan, Yada Zhu, Hanghang Tong
We theoretically prove that COIN is able to effectively increase the mutual information of node embeddings and COIN is upper-bounded by the prior distributions of nodes.
1 code implementation • ICLR 2022 • Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He
To overcome this challenge, a series of neural bandit algorithms have been proposed, where a neural network is used to learn the underlying reward function and TS or UCB are adapted for exploration.
1 code implementation • 1 Oct 2021 • Jinning Li, Huajie Shao, Dachun Sun, Ruijie Wang, Yuchen Yan, Jinyang Li, Shengzhong Liu, Hanghang Tong, Tarek Abdelzaher
Inspired by total correlation in information theory, we propose the Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to project both users and content items (e. g., posts that represent user views) into an appropriate disentangled latent space.
no code implementations • 23 Jul 2018 • Ruijie Wang, Yuchen Yan, Jialu Wang, Yuting Jia, Ye Zhang, Wei-Nan Zhang, Xinbing Wang
Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine processing.