Publications (102)
Progressive Agent Skill Generation via Reinforcement Learning
Junhao Shen, Zhanqiu Zhang, Yiwen Guo +1
Existing skill generation methods largely rely on heuristics or pipeline-style consolidation, which must be specially designed for different evidence sources. In contrast, learning…
Evaluating Progress in Graph Foundation Models: A Comprehensive Benchmark and New Insights
Xingtong Yu, Shenghua Ye, Ruijuan Liang +4
Graph foundation models (GFM) aim to acquire transferable knowledge by pre-training on diverse graphs, which can be adapted to various downstream tasks. However, domain shift in gr…
Accelerating Maximal Clique Enumeration via Graph Reduction
Wen Deng, Weiguo Zheng, Hong Cheng
As a fundamental task in graph data management, maximal clique enumeration (MCE) has attracted extensive attention from both academic and industrial communities due to its wide ran…
A Survey of Graph Transformers: Architectures, Theories and Applications
Chaohao Yuan, Kangfei Zhao, Ercan Engin Kuruoglu +6
Graph Transformers (GTs) have demonstrated a strong capability in modeling graph structures by addressing the intrinsic limitations of graph neural networks (GNNs), such as over-sm…
An Efficient Two-Stage Sparse Representation Method
Chengyu Peng, Hong Cheng, Manchor Ko
There are a large number of methods for solving under-determined linear inverse problem. Many of them have very high time complexity for large datasets. We propose a new method cal…
When Do LLMs Help With Node Classification? A Comprehensive Analysis
Xixi Wu, Yifei Shen, Fangzhou Ge +4
Node classification is a fundamental task in graph analysis, with broad applications across various fields. Recent breakthroughs in Large Language Models (LLMs) have enabled LLM-ba…
DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation
Minbin Huang, Yanxin Long, Xinchi Deng +6
Text-to-image (T2I) generation models have significantly advanced in recent years. However, effective interaction with these models is challenging for average users due to the need…
Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale
Qichao Wang, Tian Bian, Yian Yin +6
The recent surge in the research of diffusion models has accelerated the adoption of text-to-image models in various Artificial Intelligence Generated Content (AIGC) commercial pro…
Global and Local Structure Preserving Sparse Subspace Learning: An Iterative Approach to Unsupervised Feature Selection
Nan Zhou, Yangyang Xu, Hong Cheng +2
As we aim at alleviating the curse of high-dimensionality, subspace learning is becoming more popular. Existing approaches use either information about global or local structure of…
GraphReAct: Reasoning and Acting for Multi-step Graph Inference
Xingtong Yu, Zhongwei Kuai, Chang Zhou +6
Reasoning-acting frameworks enhance large language models (LLMs) by interleaving reasoning with actions for dynamic information acquisition. However, extending this paradigm to gra…
Incipient Fault Detection in Power Distribution System: A Time-Frequency Embedded Deep Learning Based Approach
Qiyue Li, Huan Luo, Hong Cheng +4
Incipient fault detection in power distribution systems is crucial to improve the reliability of the grid. However, the non-stationary nature and the inadequacy of the training dat…
Joint Embedding in Named Entity Linking on Sentence Level
Wei Shi, Siyuan Zhang, Zhiwei Zhang +2
Named entity linking is to map an ambiguous mention in documents to an entity in a knowledge base. The named entity linking is challenging, given the fact that there are multiple c…
Approximate Closest Community Search in Networks
Xin Huang, Laks V. S. Lakshmanan, Jeffrey Xu Yu +1
Recently, there has been significant interest in the study of the community search problem in social and information networks: given one or more query nodes, find densely connected…
ParaFormer: A Generalized PageRank Graph Transformer for Graph Representation Learning
Chaohao Yuan, Zhenjie Song, Ercan Engin Kuruoglu +5
Graph Transformers (GTs) have emerged as a promising graph learning tool, leveraging their all-pair connected property to effectively capture global information. To address the ove…
IBCircuit: Towards Holistic Circuit Discovery with Information Bottleneck
Tian Bian, Yifan Niu, Chaohao Yuan +7
Circuit discovery has recently attracted attention as a potential research direction to explain the non-trivial behaviors of language models. It aims to find the computational subg…
Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis
Qunzhong Wang, Xiangguo Sun, Hong Cheng
In recent years, graph prompting has emerged as a promising research direction, enabling the learning of additional tokens or subgraphs appended to the original graphs without requ…
Getting More Juice Out of Your Data: Hard Pair Refinement Enhances Visual-Language Models Without Extra Data
Haonan Wang, Minbin Huang, Runhui Huang +7
Contrastive Language-Image Pre-training (CLIP) has become the standard for cross-modal image-text representation learning. Improving CLIP typically requires additional data and ret…
Answer-Consistent Chain-of-thought Reinforcement Learning For Multi-modal Large Langauge Models
Minbin Huang, Runhui Huang, Chuanyang Zheng +4
Recent advances in large language models (LLMs) have demonstrated that reinforcement learning with verifiable rewards (RLVR) can significantly enhance reasoning abilities by direct…
User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems
Yang Deng, Wenxuan Zhang, Wai Lam +2
User Satisfaction Estimation (USE) is an important yet challenging task in goal-oriented conversational systems. Whether the user is satisfied with the system largely depends on th…
WeKnow-RAG: An Adaptive Approach for Retrieval-Augmented Generation Integrating Web Search and Knowledge Graphs
Weijian Xie, Xuefeng Liang, Yuhui Liu +3
Large Language Models (LLMs) have greatly contributed to the development of adaptive intelligent agents and are positioned as an important way to achieve Artificial General Intelli…
Decision Support System for Chronic Diseases Based on Drug-Drug Interactions
Tian Bian, Yuli Jiang, Jia Li +6
Many patients with chronic diseases resort to multiple medications to relieve various symptoms, which raises concerns about the safety of multiple medication use, as severe drug-dr…
Fourier Geometric Wind Power Forecasting with Numerical Weather Prediction
Shiyuan Piao, Fan Zehui, Yang Liu +4
Accurate short-term wind power forecasting is essential for grid stability and operational planning, yet remains challenging due to the complex interactions between atmospheric con…
When LLM Meets Hypergraph: A Sociological Analysis on Personality via Online Social Networks
Zhiyao Shu, Xiangguo Sun, Hong Cheng
Individual personalities significantly influence our perceptions, decisions, and social interactions, which is particularly crucial for gaining insights into human behavior pattern…
Decouple before Integration: Test-time Synthesis of SFT and RLVR Task Vectors
Chaohao Yuan, Chenghao Xiao, Yu Rong +2
SFT and RLVR represent two fundamental yet distinct paradigms for LLM post-training, each excelling in distinct dimensions. SFT expands knowledge breadth while RLVR enhances reason…
EvoFlow: Evolving Diverse Agentic Workflows On The Fly
Guibin Zhang, Kaijie Chen, Guancheng Wan +5
The past two years have witnessed the evolution of large language model (LLM)-based multi-agent systems from labor-intensive manual design to partial automation (\textit{e.g.}, pro…
ProG: A Graph Prompt Learning Benchmark
Chenyi Zi, Haihong Zhao, Xiangguo Sun +3
Artificial general intelligence on graphs has shown significant advancements across various applications, yet the traditional 'Pre-train & Fine-tune' paradigm faces inefficiencies…
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective
Jia Li, Yu Rong, Hong Cheng +3
Node classification and graph classification are two graph learning problems that predict the class label of a node and the class label of a graph respectively. A node of a graph u…
Structured Low-Rank Matrix Factorization with Missing and Grossly Corrupted Observations
Fanhua Shang, Yuanyuan Liu, Hanghang Tong +2
Recovering low-rank and sparse matrices from incomplete or corrupted observations is an important problem in machine learning, statistics, bioinformatics, computer vision, as well…
CrossEarth-Gate: Fisher-Guided Adaptive Tuning Engine for Efficient Adaptation of Cross-Domain Remote Sensing Semantic Segmentation
Shilei Cao, Ziyang Gong, Hehai Lin +10
In Remote Sensing (RS), Parameter-Efficient Fine-Tuning (PEFT) has emerged as a key approach to activate the generalizable representation ability of foundation models for downstrea…
Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe
Xixi Wu, Qianguo Sun, Ruiyang Zhang +4
Reinforcement Learning (RL) is essential for evolving Large Language Models (LLMs) into autonomous agents capable of long-horizon planning, yet a practical recipe for scaling RL in…
A Survey of Graph Meets Large Language Model: Progress and Future Directions
Yuhan Li, Zhixun Li, Peisong Wang +4
Graph plays a significant role in representing and analyzing complex relationships in real-world applications such as citation networks, social networks, and biological data. Recen…
GPU Accelerated Color Correction and Frame Warping for Real-time Video Stitching
Lu Yang, Zhenglun Kong, Ting Li +3
Traditional image stitching focuses on a single panorama frame without considering the spatial-temporal consistency in videos. The straightforward image stitching approach will cau…
ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization
Xixi Wu, Kuan Li, Yida Zhao +13
Large Language Model (LLM)-based web agents excel at knowledge-intensive tasks but face a fundamental conflict between the need for extensive exploration and the constraints of lim…
Can LLMs Alleviate Catastrophic Forgetting in Graph Continual Learning? A Systematic Study
Ziyang Cheng, Zhixun Li, Yuhan Li +6
Nowadays, real-world data, including graph-structure data, often arrives in a streaming manner, which means that learning systems need to continuously acquire new knowledge without…
AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models
Rui Zhang, Yixin Su, Bayu Distiawan Trisedya +4
The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-bas…
K-Reach: Who is in Your Small World
James Cheng, Zechao Shang, Hong Cheng +2
We study the problem of answering k-hop reachability queries in a directed graph, i.e., whether there exists a directed path of length k, from a source query vertex to a target que…
Target-point Attention Transformer: A novel trajectory predict network for end-to-end autonomous driving
Jingyu Du, Yang Zhao, Hong Cheng
In the field of autonomous driving, there have been many excellent perception models for object detection, semantic segmentation, and other tasks, but how can we effectively use th…
Cascade Graph Neural Networks for RGB-D Salient Object Detection
Ao Luo, Xin Li, Fan Yang +3
In this paper, we study the problem of salient object detection (SOD) for RGB-D images using both color and depth information.A major technical challenge in performing salient obje…
Theoretical Simulation of 87Rb Absorption Spectrum in a Thermal Cell
Hong Cheng, Shan-Shan Zhang, Pei-Pei Xin +2
In this paper, we present a theoretical simulation of 87Rb absorption spectrum in a thermal cm-cell which is adaptive to the experimental observation. In experiment, the coupling a…
FocusDiffuser: Perceiving Local Disparities for Camouflaged Object Detection
Jianwei Zhao, Xin Li, Fan Yang +4
Detecting objects seamlessly blended into their surroundings represents a complex task for both human cognitive capabilities and advanced artificial intelligence algorithms. Curren…
SteerX: Disentangled Steering for LLM Personalization
Xiaoyan Zhao, Ming Yan, Yilun Qiu +5
Large language models (LLMs) have shown remarkable success in recent years, enabling a wide range of applications, including intelligent assistants that support users' daily life a…
NextQuill: Causal Preference Modeling for Enhancing LLM Personalization
Xiaoyan Zhao, Juntao You, Yang Zhang +5
Personalizing large language models (LLMs) for individual users has become increasingly important as they are progressively integrated into real-world applications to support users…
CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning
Haohua Niu, Xingtong Yu, Yang Liu +6
Graph learning under distribution shift presents a persistent challenge, where models adapt to new graphs with limited or even no supervision. Recent graph--LLM approaches move tow…
Can Graph Learning Improve Planning in LLM-based Agents?
Xixi Wu, Yifei Shen, Caihua Shan +8
Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs). It aims to break down complex user requests i…
Hybrid Graph Neural Networks for Crowd Counting
Ao Luo, Fan Yang, Xin Li +4
Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale…
Co-visual pattern augmented generative transformer learning for automobile geo-localization
Jianwei Zhao, Qiang Zhai, Pengbo Zhao +2
Geolocation is a fundamental component of route planning and navigation for unmanned vehicles, but GNSS-based geolocation fails under denial-of-service conditions. Cross-view geo-l…
Unstructured Knowledge Access in Task-oriented Dialog Modeling using Language Inference, Knowledge Retrieval and Knowledge-Integrative Response Generation
Mudit Chaudhary, Borislav Dzodzo, Sida Huang +10
Dialog systems enriched with external knowledge can handle user queries that are outside the scope of the supporting databases/APIs. In this paper, we follow the baseline provided…
Adversarial Flow Matching for Imperceptible Attacks on End-to-End Autonomous Driving
Xinyu Zeng, Xiangkun He, Lei Tao +2
Autonomous driving (AD) is evolving towards end-to-end (E2E) frameworks through two primary paradigms: monolithic models exemplified by Vision-Language-Action (VLA), and specialize…
All in One: Multi-Task Prompting for Graph Neural Networks (Extended Abstract)
Xiangguo Sun, Hong Cheng, Jia Li +2
This paper is an extended abstract of our original work published in KDD23, where we won the best research paper award (Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan. A…
Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models
Xiaoyan Zhao, Yang Deng, Wenjie Wang +5
Conversational Recommender Systems (CRSs) engage users in multi-turn interactions to deliver personalized recommendations. The emergence of large language models (LLMs) further enh…
Lingshu-Cell: A generative cellular world model for transcriptome modeling toward virtual cells
Han Zhang, Guo-Hua Yuan, Chaohao Yuan +6
Modeling cellular states and predicting their responses to perturbations are central challenges in computational biology and the development of virtual cells. Existing foundation m…
Predicting Path Failure In Time-Evolving Graphs
Jia Li, Zhichao Han, Hong Cheng +4
In this paper we use a time-evolving graph which consists of a sequence of graph snapshots over time to model many real-world networks. We study the path classification problem in…
Graph Prompt Learning: A Comprehensive Survey and Beyond
Xiangguo Sun, Jiawen Zhang, Xixi Wu +3
Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration with graph data, a cornerstone in our interconnected world, remains nascent. This pape…
TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs
Rui Zhang, Xiaoyan Zhao, Bayu Distiawan Trisedya +3
The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-bas…
Query Driven-Graph Neural Networks for Community Search: From Non-Attributed, Attributed, to Interactive Attributed
Yuli Jiang, Yu Rong, Hong Cheng +3
Given one or more query vertices, Community Search (CS) aims to find densely intra-connected and loosely inter-connected structures containing query vertices. Attributed Community…
Reinforcement Learning Based Robust Volt/Var Control in Active Distribution Networks With Imprecisely Known Delay
Hong Cheng, Huan Luo, Zhi Liu +3
Active distribution networks (ADNs) incorporating massive photovoltaic (PV) devices encounter challenges of rapid voltage fluctuations and potential violations. Due to the fluctuat…
MExD: An Expert-Infused Diffusion Model for Whole-Slide Image Classification
Jianwei Zhao, Xin Li, Fan Yang +5
Whole Slide Image (WSI) classification poses unique challenges due to the vast image size and numerous non-informative regions, which introduce noise and cause data imbalance durin…
Counter-Empirical Attacking based on Adversarial Reinforcement Learning for Time-Relevant Scoring System
Xiangguo Sun, Hong Cheng, Hang Dong +3
Scoring systems are commonly seen for platforms in the era of big data. From credit scoring systems in financial services to membership scores in E-commerce shopping platforms, pla…
The characterizations on a class of weakly weighted Einstein-Finsler metrics
Xinyue Cheng, Hong Cheng, Pengsheng Wu
In this paper, we study the weakly weighted Einstein-Finsler metrics. First, we show that weakly weighted Einstein-Kropina metrics must be of isotropic S-curvature with respect to…
Graph Autoencoders with Deconvolutional Networks
Jia Li, Tomas Yu, Da-Cheng Juan +3
Recent studies have indicated that Graph Convolutional Networks (GCNs) act as a \emph{low pass} filter in spectral domain and encode smoothed node representations. In this paper, w…
Protein Multimer Structure Prediction via Prompt Learning
Ziqi Gao, Xiangguo Sun, Zijing Liu +3
Understanding the 3D structures of protein multimers is crucial, as they play a vital role in regulating various cellular processes. It has been empirically confirmed that the mult…
Understanding the Behaviors of Environment-aware Information Retrieval
Ruifeng Yuan, Chaohao Yuan, David Dai +4
Recent retrieval-augmented generation (RAG) approaches have demonstrated strong capability in handling complex queries, yet current research overlooks a critical challenge: differe…
Random-walk domination in large graphs: problem definitions and fast solutions
Rong-Hua Li, Jeffrey Xu Yu, Xin Huang +1
We introduce and formulate two types of random-walk domination problems in graphs motivated by a number of applications in practice (e.g., item-placement problem in online social n…
Sparse Bayesian Dictionary Learning with a Gaussian Hierarchical Model
Linxiao Yang, Jun Fang, Hong Cheng +1
We consider a dictionary learning problem whose objective is to design a dictionary such that the signals admits a sparse or an approximate sparse representation over the learned d…
Adaptive Graph Integration for Cross-Domain Recommendation via Heterogeneous Graph Coordinators
Hengyu Zhang, Chunxu Shen, Xiangguo Sun +5
In the digital era, users typically interact with diverse items across multiple domains (e.g., e-commerce, streaming platforms, and social networks), generating intricate heterogen…
All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining
Haihong Zhao, Aochuan Chen, Xiangguo Sun +2
Large Language Models (LLMs) have revolutionized the fields of computer vision (CV) and natural language processing (NLP). One of the most notable advancements of LLMs is that a si…
Dirichlet Graph Variational Autoencoder
Jia Li, Tomasyu Yu, Jiajin Li +5
Graph Neural Networks (GNNs) and Variational Autoencoders (VAEs) have been widely used in modeling and generating graphs with latent factors. However, there is no clear explanation…
Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation
Hengyu Zhang, Chunxu Shen, Xiangguo Sun +5
In real-world recommendation scenarios, users typically engage with platforms through multiple types of behavioral interactions. Multi-behavior recommendation algorithms aim to lev…
Beyond Structure: Invariant Crystal Property Prediction with Pseudo-Particle Ray Diffraction
Bin Cao, Yang Liu, Longhan Zhang +6
Crystal property prediction, governed by quantum mechanical principles, is computationally prohibitive to solve exactly for large many-body systems using traditional density functi…
UniPool: A Globally Shared Expert Pool for Mixture-of-Experts
Minbin Huang, Han Shi, Chuanyang Zheng +5
Modern Mixture-of-Experts (MoE) architectures allocate expert capacity through a rigid per-layer rule: each transformer layer owns a separate expert set. This convention couples de…
The thermal power generation and economic growth in the central and western China: A heterogeneous mixed panel Granger-Causality approach
Jie Ni, Jiayi Qian, Yixiao Lu +1
The problem of the new energy economy has become a global hot issue. This study examines the causal relationship between the ratio of thermal power in total power generation (RTPG)…
Reinforced Strategy Optimization for Conversational Recommender Systems via Network-of-Experts
Xiaoyan Zhao, Ming Yan, Yang Zhang +6
Conversational Recommender Systems (CRSs) aim to provide personalized recommendations through multi-turn natural language interactions with users. Given the strong interaction and…
Vision-Language Meets the Skeleton: Progressively Distillation with Cross-Modal Knowledge for 3D Action Representation Learning
Yang Chen, Tian He, Junfeng Fu +4
Skeleton-based action representation learning aims to interpret and understand human behaviors by encoding the skeleton sequences, which can be categorized into two primary trainin…
A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers
Xiaoyan Zhao, Yang Deng, Min Yang +6
Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and inf…
AgroOmni: A Large-Scale Multi-view Agricultural Dataset for Cross-Scale Multimodal Reasoning
Jiarui Zhang, Junqi Hu, Zurong Mai +10
Modern agricultural data is sourced from diverse platforms and spans multiple spatial scales, ranging from ground-level close-up photography to Unmanned Aerial Vehicle (UAV) aerial…
Natural Language-Assisted Multi-modal Medication Recommendation
Jie Tan, Yu Rong, Kangfei Zhao +5
Combinatorial medication recommendation(CMR) is a fundamental task of healthcare, which offers opportunities for clinical physicians to provide more precise prescriptions for patie…
Distributional Inclusion Hypothesis and Quantifications: Probing for Hypernymy in Functional Distributional Semantics
Chun Hei Lo, Wai Lam, Hong Cheng +1
Functional Distributional Semantics (FDS) models the meaning of words by truth-conditional functions. This provides a natural representation for hypernymy but no guarantee that it…
Graph Iso/Auto-morphism: A Divide-&-Conquer Approach
Can Lu, Jeffrey Xu Yu, Zhiwei Zhang +1
The graph isomorphism is to determine whether two graphs are isomorphic. A closely related problem is automorphism detection, where an isomorphism between two graphs is a bijection…
Link Prediction via Matrix Completion
Ratha Pech, Dong Hao, Liming Pan +2
Inspired by practical importance of social networks, economic networks, biological networks and so on, studies on large and complex networks have attracted a surge of attentions in…
A Framework of Algorithms: Computing the Bias and Prestige of Nodes in Trust Networks
Rong-Hua Li, Jeffrey Xu Yu, Xin Huang +1
A trust network is a social network in which edges represent the trust relationship between two nodes in the network. In a trust network, a fundamental question is how to assess an…
Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization
Yilun Qiu, Xiaoyan Zhao, Yang Zhang +5
Personalizing Large Language Models (LLMs) has become a critical step in facilitating their widespread application to enhance individual life experiences. In pursuit of personaliza…
Close-up View synthesis by Interpolating Optical Flow
Xinyi Bai, Ze Wang, Lu Yang +1
The virtual viewpoint is perceived as a new technique in virtual navigation, as yet not supported due to the lack of depth information and obscure camera parameters. In this paper,…
Deconvolutional Networks on Graph Data
Jia Li, Jiajin Li, Yang Liu +3
In this paper, we consider an inverse problem in graph learning domain -- ``given the graph representations smoothed by Graph Convolutional Network (GCN), how can we reconstruct th…
Mutual Graph Learning for Camouflaged Object Detection
Qiang Zhai, Xin Li, Fan Yang +3
Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models. A major challenge is that the intrinsic similarities between suc…
AGCD: Agent-Guided Cross-Modal Decoding for Weather Forecasting
Jing Wu, Yang Liu, Lin Zhang +13
Accurate weather forecasting is more than grid-wise regression: it must preserve coherent synoptic structures and physical consistency of meteorological fields, especially under au…
Can Large Language Models Be Query Optimizer for Relational Databases?
Jie Tan, Kangfei Zhao, Rui Li +6
Query optimization, which finds the optimized execution plan for a given query, is a complex planning and decision-making problem within the exponentially growing plan space in dat…
Regularized Orthogonal Tensor Decompositions for Multi-Relational Learning
Fanhua Shang, James Cheng, Hong Cheng
Multi-relational learning has received lots of attention from researchers in various research communities. Most existing methods either suffer from superlinear per-iteration cost,…
Partner Personas Generation for Diverse Dialogue Generation
Hongyuan Lu, Wai Lam, Hong Cheng +1
Incorporating personas information allows diverse and engaging responses in dialogue response generation. Unfortunately, prior works have primarily focused on self personas and hav…
All in One: Multi-task Prompting for Graph Neural Networks
Xiangguo Sun, Hong Cheng, Jia Li +2
Recently, ''pre-training and fine-tuning'' has been adopted as a standard workflow for many graph tasks since it can take general graph knowledge to relieve the lack of graph annot…
Mask-GVAE: Blind Denoising Graphs via Partition
Jia Li, Mengzhou Liu, Honglei Zhang +4
We present Mask-GVAE, a variational generative model for blind denoising large discrete graphs, in which "blind denoising" means we don't require any supervision from clean graphs.…
TianQuan-S2S: A Subseasonal-to-Seasonal Global Weather Model via Incorporate Climatology State
Guowen Li, Xintong Liu, Yang Liu +11
Accurate Subseasonal-to-Seasonal (S2S) forecasting is vital for decision-making in agriculture, energy production, and emergency management. However, it remains a challenging and u…
Fast Distributed Complex Join Processing
Hao Zhang, Miao Qiao, Jeffrey Xu Yu +1
In this work, we study the problem of co-optimize communication, pre-computing, and computation cost in one-round multi-way join evaluation. We propose a multi-way join approach AD…
MMLF: Multi-modal Multi-class Late Fusion for Object Detection with Uncertainty Estimation
Qihang Yang, Yang Zhao, Hong Cheng
Autonomous driving necessitates advanced object detection techniques that integrate information from multiple modalities to overcome the limitations associated with single-modal ap…
Self-supervised Hypergraph Representation Learning for Sociological Analysis
Xiangguo Sun, Hong Cheng, Bo Liu +4
Modern sociology has profoundly uncovered many convincing social criteria for behavioural analysis. Unfortunately, many of them are too subjective to be measured and presented in o…
Backward Path Growth for Efficient Mobile Sequential Recommendation
Jianbin Huang, Xuejun Huangfu, Heli Sun +2
The problem of mobile sequential recommendation is presented to suggest a route connecting some pick-up points for a taxi driver so that he/she is more likely to get passengers wit…
MoLoRAG: Bootstrapping Document Understanding via Multi-modal Logic-aware Retrieval
Xixi Wu, Yanchao Tan, Nan Hou +2
Document Understanding is a foundational AI capability with broad applications, and Document Question Answering (DocQA) is a key evaluation task. Traditional methods convert the do…
Adversarial Attack on Community Detection by Hiding Individuals
Jia Li, Honglei Zhang, Zhichao Han +3
It has been demonstrated that adversarial graphs, i.e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks. In th…
WeatherSyn: An Instruction Tuning MLLM For Weather Forecasting Report Generation
Zinan Zheng, Yang Liu, Nuo Chen +3
Accurate weather forecast reporting enables individuals and communities to better plan daily activities and agricultural operations. However, the current reporting process primaril…
Modified Topological Image Preprocessing for Skin Lesion Classifications
Hong Cheng, Rebekah Leamons, Ahmad Al Shami
This paper proposes a modified Topological Data Analysis model for skin images preprocessing and enhancements. The skin lesion dataset HAM10000 used with the intention of identifyi…
Dynamic Skill Lifecycle Management for Agentic Reinforcement Learning
Junhao Shen, Teng Zhang, Xiaoyan Zhao +1
Large language model agents increasingly rely on external skills to solve complex tasks, where skills act as modular units that extend their capabilities beyond what parametric mem…