30 citations · 213 across the 50 of their papers we have counts for
11 papers · 1 filter
Does Few-Shot Learning Help LLM Performance in Code Synthesis?
Derek Xu, Tong Xie, Botao Xia +4
Large language models (LLMs) have made significant strides at code generation through improved model design, training, and chain-of-thought. However, prompt-level optimizations rem…
Non-Euclidean Mixture Model for Social Network Embedding
Roshni G. Iyer, Yewen Wang, Wei Wang +1
It is largely agreed that social network links are formed due to either homophily or social influence. Inspired by this, we aim at understanding the generation of links via providi…
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…
Inverse designing metamaterials with programmable nonlinear functional responses in graph space
Marco Maurizi, Derek Xu, Yu-Tong Wang +9
Material responses to static and dynamic stimuli, represented as nonlinear curves, are design targets for engineering functionalities like structural support, impact protection, an…
Mixture of In-Context Prompters for Tabular PFNs
Derek Xu, Olcay Cirit, Reza Asadi +2
Recent benchmarks found In-Context Learning (ICL) outperforms both deep learning and tree-based algorithms on small tabular datasets. However, on larger datasets, ICL for tabular l…
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…
Hierarchical Attention Models for Multi-Relational Graphs
Roshni G. Iyer, Wei Wang, Yizhou Sun
We present Bi-Level Attention-Based Relational Graph Convolutional Networks (BR-GCN), unique neural network architectures that utilize masked self-attentional layers with relationa…
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…
An Evaluation of Large Language Models in Bioinformatics Research
Hengchuang Yin, Zhonghui Gu, Fanhao Wang +6
Large language models (LLMs) such as ChatGPT have gained considerable interest across diverse research communities. Their notable ability for text completion and generation has ina…