no code implementations • EMNLP 2020 • Zhengjue Wang, Zhibin Duan, Hao Zhang, Chaojie Wang, Long Tian, Bo Chen, Mingyuan Zhou
Abstractive document summarization is a comprehensive task including document understanding and summary generation, in which area Transformer-based models have achieved the state-of-the-art performance.
no code implementations • 31 Dec 2023 • Chaojie Wang, Yishi Xu, Zhong Peng, Chenxi Zhang, Bo Chen, Xinrun Wang, Lei Feng, Bo An
Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, especially for question answering.
1 code implementation • 20 Sep 2022 • Dongsheng Wang, Yishi Xu, Miaoge Li, Zhibin Duan, Chaojie Wang, Bo Chen, Mingyuan Zhou
We propose a Bayesian generative model for incorporating prior domain knowledge into hierarchical topic modeling.
no code implementations • 12 Sep 2022 • Dongsheng Wang, Chaojie Wang, Bo Chen, Mingyuan Zhou
To build recommender systems that not only consider user-item interactions represented as ordinal variables, but also exploit the social network describing the relationships between the users, we develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions.
2 code implementations • 28 May 2022 • Tian Lv, Chongyang Bai, Chaojie Wang
To resolve it, we present (i) multi-dimensional MLP (MDMLP), a conceptually simple and lightweight MLP-based architecture yet achieves SOTA when training from scratch on small-size datasets; (ii) multi-dimension MLP Attention Tool (MDAttnTool), a novel and efficient attention mechanism based on MLPs.
1 code implementation • 7 Feb 2022 • Yilin He, Chaojie Wang, Hao Zhang, Bo Chen, Mingyuan Zhou
This paper introduces a graph generative process to model how the observed edges are generated by aggregating the node interactions over a set of overlapping node communities, each of which contributes to the edges via a logical OR mechanism.
1 code implementation • NeurIPS 2021 • Zhibin Duan, Yishi Xu, Bo Chen, Dongsheng Wang, Chaojie Wang, Mingyuan Zhou
Existing deep hierarchical topic models are able to extract semantically meaningful topics from a text corpus in an unsupervised manner and automatically organize them into a topic hierarchy.
no code implementations • 29 Sep 2021 • Yilin He, Chaojie Wang, Hao Zhang, Bo Chen, Mingyuan Zhou
In this paper, we introduce a relational graph generative process to model how the observed edges are generated by aggregating the node interactions over multiple overlapping node communities, each of which represents a particular type of relation that contributes to the edges via a logical OR mechanism.
1 code implementation • 12 Sep 2021 • Yongrui Chen, Xinnan Guo, Chaojie Wang, Jian Qiu, Guilin Qi, Meng Wang, Huiying Li
Compared to the larger pre-trained model and the tabular-specific pre-trained model, our approach is still competitive.
1 code implementation • ACL 2021 • Zhibin Duan, Hao Zhang, Chaojie Wang, Zhengjue Wang, Bo Chen, Mingyuan Zhou
As a result, the backbone learns the shared knowledge among all clusters while modulated weights extract the cluster-specific features.
1 code implementation • 30 Jun 2021 • Zhibin Duan, Dongsheng Wang, Bo Chen, Chaojie Wang, Wenchao Chen, Yewen Li, Jie Ren, Mingyuan Zhou
However, they often assume in the prior that the topics at each layer are independently drawn from the Dirichlet distribution, ignoring the dependencies between the topics both at the same layer and across different layers.
no code implementations • 1 Feb 2021 • Chaojie Wang, Srinivas Peeta, Jian Wang
The first stage incorporates a decentralized local route switching dynamical system to approximate the system optimal route flow in a local area based on vehicles' knowledge of local traffic information.
Autonomous Vehicles Physics and Society Computer Science and Game Theory Systems and Control Systems and Control
no code implementations • NeurIPS 2020 • Chaojie Wang, Hao Zhang, Bo Chen, Dongsheng Wang, Zhengjue Wang, Mingyuan Zhou
To analyze a collection of interconnected documents, relational topic models (RTMs) have been developed to describe both the link structure and document content, exploring their underlying relationships via a single-layer latent representation with limited expressive capability.
1 code implementation • NeurIPS 2020 • Wenchao Chen, Chaojie Wang, Bo Chen, Yicheng Liu, Hao Zhang, Mingyuan Zhou
Incorporating the natural document-sentence-word structure into hierarchical Bayesian modeling, we propose convolutional Poisson gamma dynamical systems (PGDS) that introduce not only word-level probabilistic convolutions, but also sentence-level stochastic temporal transitions.
no code implementations • 28 Sep 2020 • Dandan Guo, Bo Chen, Wenchao Chen, Chaojie Wang, Hongwei Liu, Mingyuan Zhou
We develop a recurrent gamma belief network (rGBN) for radar automatic target recognition (RATR) based on high-resolution range profile (HRRP), which characterizes the temporal dependence across the range cells of HRRP.
1 code implementation • 14 May 2019 • Chaojie Wang, Bo Chen, Sucheng Xiao, Mingyuan Zhou
For text analysis, one often resorts to a lossy representation that either completely ignores word order or embeds each word as a low-dimensional dense feature vector.