1 code implementation • 6 Jan 2025 • Ji Cao, Tongya Zheng, Qinghong Guo, Yu Wang, Junshu Dai, Shunyu Liu, Jie Yang, Jie Song, Mingli Song
Trajectory generation has garnered significant attention from researchers in the field of spatio-temporal analysis, as it can generate substantial synthesized human mobility trajectories that enhance user privacy and alleviate data scarcity.
2 code implementations • 24 Dec 2024 • Huanjin Yao, Jiaxing Huang, Wenhao Wu, Jingyi Zhang, Yibo Wang, Shunyu Liu, Yingjie Wang, Yuxin Song, Haocheng Feng, Li Shen, DaCheng Tao
Using CoMCTS, we construct Mulberry-260k, a multimodal dataset with a tree of rich, explicit and well-defined reasoning nodes for each question.
1 code implementation • 13 Aug 2024 • Shuang Luo, Yinchuan Li, Shunyu Liu, Xu Zhang, Yunfeng Shao, Chao Wu
Generative Flow Networks (GFlowNets) aim to generate diverse trajectories from a distribution in which the final states of the trajectories are proportional to the reward, serving as a powerful alternative to reinforcement learning for exploratory control tasks.
1 code implementation • 22 Jul 2024 • Shunyu Liu, Yaoru Li, Kongcheng Zhang, Zhenyu Cui, Wenkai Fang, Yuxuan Zheng, Tongya Zheng, Mingli Song
In this work, we introduce Odyssey, a new framework that empowers Large Language Model (LLM)-based agents with open-world skills to explore the vast Minecraft world.
1 code implementation • 13 Jul 2024 • Wenda Li, KaiXuan Chen, Shunyu Liu, Tongya Zheng, Wenjie Huang, Mingli Song
Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has demonstrated significant advantages in semi-supervised node prediction tasks with improved computational efficiency and enhanced model robustness.
1 code implementation • 2 Jul 2024 • Yuwen Wang, Shunyu Liu, Tongya Zheng, KaiXuan Chen, Mingli Song
Graph Neural Networks (GNNs) have emerged as a prominent framework for graph mining, leading to significant advances across various domains.
1 code implementation • 25 Jun 2024 • Feiyang Xu, Shunyu Liu, Yunpeng Qing, Yihe Zhou, Yuwen Wang, Mingli Song
In this paper, we propose a novel temporal prototype-aware learning method, abbreviated as TPA, to learn time-adaptive AVC under short-term training trajectories.
no code implementations • 13 Jun 2024 • Jiacong Hu, Jingwen Ye, Zunlei Feng, Jiazhen Yang, Shunyu Liu, Xiaotian Yu, Lingxiang Jia, Mingli Song
Recognizing that a correct prediction relies on the correctness of the latent feature's pattern, we introduce a novel and effective Feature Pattern Consistency Constraint (FPCC) method to reinforce the latent feature's capacity to maintain the correct feature pattern.
no code implementations • 28 May 2024 • Yutao Yang, Jie zhou, Xuanwen Ding, Tianyu Huai, Shunyu Liu, Qin Chen, Yuan Xie, Liang He
Recently, foundation language models (LMs) have marked significant achievements in the domains of natural language processing (NLP) and computer vision (CV).
no code implementations • 24 May 2024 • Shunyu Liu, Wei Luo, Yanzhen Zhou, KaiXuan Chen, Quan Zhang, Huating Xu, Qinglai Guo, Mingli Song
Transmission interface power flow adjustment is a critical measure to ensure the security and economy operation of power systems.
1 code implementation • 22 Mar 2024 • Zhenbang Xiao, Yu Wang, Shunyu Liu, Huiqiong Wang, Mingli Song, Tongya Zheng
The burdensome training costs on large-scale graphs have aroused significant interest in graph condensation, which involves tuning Graph Neural Networks (GNNs) on a small condensed graph for use on the large-scale original graph.
1 code implementation • 12 Mar 2024 • Yunpeng Qing, Shunyu Liu, Jingyuan Cong, KaiXuan Chen, Yihe Zhou, Mingli Song
Offline reinforcement learning endeavors to leverage offline datasets to craft effective agent policy without online interaction, which imposes proper conservative constraints with the support of behavior policies to tackle the out-of-distribution problem.
1 code implementation • 4 Mar 2024 • Yu Wang, Tongya Zheng, Yuxuan Liang, Shunyu Liu, Mingli Song
To address these challenges, we have tailored a Cross-city mObiLity trAnsformer (COLA) with a dedicated model-agnostic transfer framework by effectively transferring cross-city knowledge for human trajectory simulation.
1 code implementation • 1 Mar 2024 • Kedi Chen, Jie zhou, Qin Chen, Shunyu Liu, Liang He
Information extraction (IE) aims to extract complex structured information from the text.
1 code implementation • 23 Feb 2024 • Shunyu Liu, Jie zhou, Qunxi Zhu, Qin Chen, Qingchun Bai, Jun Xiao, Liang He
Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
1 code implementation • 18 Jan 2024 • Zhenbang Xiao, Yu Wang, Shunyu Liu, Bingde Hu, Huiqiong Wang, Mingli Song, Tongya Zheng
Graph condensation has emerged as an intriguing technique to save the expensive training costs of Graph Neural Networks (GNNs) by substituting a condensed small graph with the original graph.
1 code implementation • 5 Jan 2024 • KaiXuan Chen, Wei Luo, Shunyu Liu, Yaoquan Wei, Yihe Zhou, Yunpeng Qing, Quan Zhang, Jie Song, Mingli Song
In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power flow adjustment across different transmission sections.
no code implementations • 28 Nov 2023 • Yaoquan Wei, Shunyu Liu, Jie Song, Tongya Zheng, KaiXuan Chen, Yong Wang, Mingli Song
Instead, we employ a proxy model to extract state features that are both discriminative (adaptive to the agent) and generally applicable (robust to agent noise).
1 code implementation • 5 Aug 2023 • Yuwen Wang, Shunyu Liu, KaiXuan Chen, Tongtian Zhu, Ji Qiao, Mengjie Shi, Yuanyu Wan, Mingli Song
Graph Lottery Ticket (GLT), a combination of core subgraph and sparse subnetwork, has been proposed to mitigate the computational cost of deep Graph Neural Networks (GNNs) on large input graphs while preserving original performance.
1 code implementation • 15 Jun 2023 • Yu Wang, Tongya Zheng, Shunyu Liu, Zunlei Feng, KaiXuan Chen, Yunzhi Hao, Mingli Song
The human mobility simulation task aims to generate human mobility trajectories given a small set of trajectory data, which have aroused much concern due to the scarcity and sparsity of human mobility data.
1 code implementation • 14 Jun 2023 • Shunyu Liu, Yunpeng Qing, Shuqi Xu, Hongyan Wu, Jiangtao Zhang, Jingyuan Cong, Tianhao Chen, YunFu Liu, Mingli Song
Inverse Reinforcement Learning (IRL) aims to reconstruct the reward function from expert demonstrations to facilitate policy learning, and has demonstrated its remarkable success in imitation learning.
no code implementations • 3 Jun 2023 • Wenda Li, KaiXuan Chen, Shunyu Liu, Wenjie Huang, Haofei Zhang, Yingjie Tian, Yun Su, Mingli Song
In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' baselines.
1 code implementation • 31 May 2023 • KaiXuan Chen, Shunyu Liu, Tongtian Zhu, Tongya Zheng, Haofei Zhang, Zunlei Feng, Jingwen Ye, Mingli Song
Graph Neural Networks (GNNs) have emerged as a powerful category of learning architecture for handling graph-structured data.
1 code implementation • 27 May 2023 • Yihe Zhou, Shunyu Liu, Yunpeng Qing, KaiXuan Chen, Tongya Zheng, Yanhao Huang, Jie Song, Mingli Song
Despite the encouraging results achieved, CTDE makes an independence assumption on agent policies, which limits agents to adopt global cooperative information from each other during centralized training.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
1 code implementation • 23 Nov 2022 • Shunyu Liu, Yihe Zhou, Jie Song, Tongya Zheng, KaiXuan Chen, Tongtian Zhu, Zunlei Feng, Mingli Song
Value Decomposition (VD) aims to deduce the contributions of agents for decentralized policies in the presence of only global rewards, and has recently emerged as a powerful credit assignment paradigm for tackling cooperative Multi-Agent Reinforcement Learning (MARL) problems.
1 code implementation • 12 Nov 2022 • Yunpeng Qing, Shunyu Liu, Jie Song, Huiqiong Wang, Mingli Song
In this survey, we provide a comprehensive review of existing works on eXplainable RL (XRL) and introduce a new taxonomy where prior works are clearly categorized into model-explaining, reward-explaining, state-explaining, and task-explaining methods.
1 code implementation • 8 Jul 2022 • Shunyu Liu, Jie Song, Yihe Zhou, Na Yu, KaiXuan Chen, Zunlei Feng, Mingli Song
In this work, we introduce a novel interactiOn Pattern disenTangling (OPT) method, to disentangle the entity interactions into interaction prototypes, each of which represents an underlying interaction pattern within a subgroup of the entities.
2 code implementations • 5 Jul 2022 • Shunyu Liu, KaiXuan Chen, Na Yu, Jie Song, Zunlei Feng, Mingli Song
Despite the promising results achieved, state-of-the-art interactive reinforcement learning schemes rely on passively receiving supervision signals from advisor experts, in the form of either continuous monitoring or pre-defined rules, which inevitably result in a cumbersome and expensive learning process.
1 code implementation • 12 May 2022 • KaiXuan Chen, Shunyu Liu, Na Yu, Rong Yan, Quan Zhang, Jie Song, Zunlei Feng, Mingli Song
As the topology of the power system is in the form of graph structure, graph neural network based representation learning is naturally suitable for learning the status of the power system.
no code implementations • 16 Dec 2021 • Gengshi Han, Shunyu Liu, KaiXuan Chen, Na Yu, Zunlei Feng, Mingli Song
This paper proposes a controllable sample generation framework based on Conditional Tabular Generative Adversarial Network (CTGAN) to generate specified transient stability samples.
1 code implementation • 29 Sep 2021 • KaiXuan Chen, Jie Song, Shunyu Liu, Na Yu, Zunlei Feng, Gengshi Han, Mingli Song
A DKEPool network de facto disassembles representation learning into two stages, structure learning and distribution learning.