1 code implementation • 31 Mar 2024 • Shiguang Wu, Wenda Wei, Mengqi Zhang, Zhumin Chen, Jun Ma, Zhaochun Ren, Maarten de Rijke, Pengjie Ren
Both methods compute relevance as a sum of products of query and document vectors and an alignment matrix.
1 code implementation • 25 Mar 2024 • Jiyuan Yang, Yuanzi Li, Jingyu Zhao, Hanbing Wang, Muyang Ma, Jun Ma, Zhaochun Ren, Mengqi Zhang, Xin Xin, Zhumin Chen, Pengjie Ren
We conduct extensive experiments to evaluate the performance of representative sequential recommendation models in the setting of lifelong sequences.
no code implementations • 18 Mar 2024 • Mengqi Zhang, Yang Fu, Zheng Ding, Sifei Liu, Zhuowen Tu, Xiaolong Wang
In this paper, we propose HOIDiffusion for generating realistic and diverse 3D hand-object interaction data.
no code implementations • 27 Feb 2024 • Pengjie Ren, Chengshun Shi, Shiguang Wu, Mengqi Zhang, Zhaochun Ren, Maarten de Rijke, Zhumin Chen, Jiahuan Pei
Parameter-efficient fine-tuning (PEFT) is a popular method for tailoring pre-trained large language models (LLMs), especially as the models' scale and the diversity of tasks increase.
no code implementations • 21 Feb 2024 • Mengqi Zhang, Xiaotian Ye, Qiang Liu, Pengjie Ren, Shu Wu, Zhumin Chen
Large language models (LLMs) are pivotal in advancing natural language processing (NLP) tasks, yet their efficacy is hampered by inaccuracies and outdated knowledge.
no code implementations • 2 Jan 2024 • Zhongkun Liu, Zheng Chen, Mengqi Zhang, Zhaochun Ren, Pengjie Ren, Zhumin Chen
Existing debiasing methods for LLMs require external bias knowledge or annotated non-biased samples, which is lacking for position debiasing and impractical in reality.
1 code implementation • 2 Aug 2023 • Zheng Ding, Mengqi Zhang, Jiajun Wu, Zhuowen Tu
Feature collage systematically crops and combines partial features of the neighboring patches to predict the features of a shifted image patch, allowing the seamless generation of the entire image due to the overlap in the patch feature space.
no code implementations • 2 Feb 2023 • Yuwei Xia, Mengqi Zhang, Qiang Liu, Shu Wu, Xiao-Yu Zhang
Most existing works focus on exploring evolutionary information in history to obtain effective temporal embeddings for entities and relations, but they ignore the variation in evolution patterns of facts, which makes them struggle to adapt to future data with different evolution patterns.
no code implementations • 9 Nov 2022 • Yangjun Wu, Kebin Fang, Yao Zhao, Hao Zhang, Lifeng Shi, Mengqi Zhang
To accomplish punctuation restoration, most existing methods focus on introducing extra information (e. g., part-of-speech) or addressing the class imbalance problem.
1 code implementation • 1 Nov 2021 • Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Mengqi Zhang, Shu Wu, Liang Wang
Although having access to multiple modalities might allow us to capture rich information, we argue that the simple coarse-grained fusion by linear combination or concatenation in previous work is insufficient to fully understand content information and item relationships. To this end, we propose a latent structure MIning with ContRastive mOdality fusion method (MICRO for brevity).
no code implementations • 16 Aug 2021 • Mengqi Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang
In our work, different views can be obtained based on the various relations among nodes.
1 code implementation • 15 Apr 2021 • Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, Liang Wang
We propose a new method named Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which connects different user sequences through a dynamic graph structure, exploring the interactive behavior of users and items with time and order information.
1 code implementation • 8 Jan 2021 • Xiaohan Li, Mengqi Zhang, Shu Wu, Zheng Liu, Liang Wang, Philip S. Yu
Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time.
3 code implementations • 20 Oct 2019 • Mengqi Zhang, Shu Wu, Meng Gao, Xin Jiang, Ke Xu, Liang Wang
The other is Dot-Product Attention mechanism, which draws on the Transformer net to explicitly model the effect of historical sessions on the current session.