30 citations · 71 across the 21 of their papers we have counts for
23 papers
FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion
Fred Xu, Song Jiang, Zijie Huang +4
Taxonomy Expansion, which models complex concepts and their relations, can be formulated as a set representation learning task. The generalization of set, fuzzy set, incorporates u…
FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation
Haixin Wang, Ruoyan Li, Fred Xu +7
Data-driven modeling of fluid dynamics has advanced rapidly with neural PDE solvers, yet a fair and strong benchmark remains fragmented due to the absence of unified PDE datasets a…
Self-Guided Diffusion Model for Accelerating Computational Fluid Dynamics
Ruoyan Li, Zijie Huang, Haixin Wang +3
Machine learning methods, such as diffusion models, are widely explored as a promising way to accelerate high-fidelity fluid dynamics computation via a super-resolution process fro…
A Social Dynamical System for Twitter Analysis
Zhiping Xiao, Xinyu Wang, Yifang Qin +3
Understanding the evolution of public opinion is crucial for informed decision-making in various domains, particularly public affairs. The rapid growth of social networks, such as…
Inferring from Logits: Exploring Best Practices for Decoding-Free Generative Candidate Selection
Mingyu Derek Ma, Yanna Ding, Zijie Huang +3
Generative Language Models rely on autoregressive decoding to produce the output sequence token by token. Many tasks such as preference optimization, require the model to produce t…
Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation
Yanna Ding, Zijie Huang, Xiao Shou +3
Learning curve extrapolation predicts neural network performance from early training epochs and has been applied to accelerate AutoML, facilitating hyperparameter tuning and neural…
Predicting Time Series of Networked Dynamical Systems without Knowing Topology
Yanna Ding, Zijie Huang, Malik Magdon-Ismail +1
Many real-world complex systems, such as epidemic spreading networks and ecosystems, can be modeled as networked dynamical systems that produce multivariate time series. Learning t…
Graph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics
Fang Sun, Zijie Huang, Haixin Wang +4
Accurately predicting long-horizon molecular dynamics (MD) trajectories remains a significant challenge, as existing deep learning methods often struggle to retain fidelity over ex…
Physics-Informed Regularization for Domain-Agnostic Dynamical System Modeling
Zijie Huang, Wanjia Zhao, Jingdong Gao +6
Learning complex physical dynamics purely from data is challenging due to the intrinsic properties of systems to be satisfied. Incorporating physics-informed priors, such as in Ham…
Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey
Haixin Wang, Yadi Cao, Zijie Huang +13
This paper explores the recent advancements in enhancing Computational Fluid Dynamics (CFD) tasks through Machine Learning (ML) techniques. We begin by introducing fundamental conc…
MIRAI: Evaluating LLM Agents for Event Forecasting
Chenchen Ye, Ziniu Hu, Yihe Deng +4
Recent advancements in Large Language Models (LLMs) have empowered LLM agents to autonomously collect world information, over which to conduct reasoning to solve complex problems.…
BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations
Kaiqiao Han, Yi Yang, Zijie Huang +8
Brain network analysis is vital for understanding the neural interactions regarding brain structures and functions, and identifying potential biomarkers for clinical phenotypes. Ho…
Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems
Zijie Huang, Jeehyun Hwang, Junkai Zhang +6
Real-world multi-agent systems are often dynamic and continuous, where the agents co-evolve and undergo changes in their trajectories and interactions over time. For example, the C…
Synergistic Signals: Exploiting Co-Engagement and Semantic Links via Graph Neural Networks
Zijie Huang, Baolin Li, Hafez Asgharzadeh +5
Given a set of candidate entities (e.g. movie titles), the ability to identify similar entities is a core capability of many recommender systems. Most often this is achieved by col…
TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems
Zijie Huang, Wanjia Zhao, Jingdong Gao +6
Learning complex multi-agent system dynamics from data is crucial across many domains, such as in physical simulations and material modeling. Extended from purely data-driven appro…
Generalizing Graph ODE for Learning Complex System Dynamics across Environments
Zijie Huang, Yizhou Sun, Wei Wang
Learning multi-agent system dynamics has been extensively studied for various real-world applications, such as molecular dynamics in biology. Most of the existing models are built…
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs
Zijie Huang, Daheng Wang, Binxuan Huang +8
Knowledge graph embeddings (KGE) have been extensively studied to embed large-scale relational data for many real-world applications. Existing methods have long ignored the fact ma…
CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems
Song Jiang, Zijie Huang, Xiao Luo +1
Multi-agent dynamical systems refer to scenarios where multiple units interact with each other and evolve collectively over time. To make informed decisions in multi-agent dynamica…
A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware
Shichang Zhang, Atefeh Sohrabizadeh, Cheng Wan +7
Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data. GNNs achieve state-of-the-art performance on many tasks, but they face scalability…
A Mobility-Aware Deep Learning Model for Long-Term COVID-19 Pandemic Prediction and Policy Impact Analysis
Danfeng Guo, Zijie Huang, Junheng Hao +3
Pandemic(epidemic) modeling, aiming at disease spreading analysis, has always been a popular research topic especially following the outbreak of COVID-19 in 2019. Some representati…
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment
Zijie Huang, Zheng Li, Haoming Jiang +6
Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete. Due to labor-intensive human labeling, this phenomenon deteriorates when handling…
DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction
Ruijie Wang, Zijie Huang, Shengzhong Liu +7
This paper describes a novel diffusion model, DyDiff-VAE, for information diffusion prediction on social media. Given the initial content and a sequence of forwarding users, DyDiff…
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations
Zijie Huang, Yizhou Sun, Wei Wang
Many real-world systems, such as moving planets, can be considered as multi-agent dynamic systems, where objects interact with each other and co-evolve along with the time. Such dy…