25 citations · 137 across the 19 of their papers we have counts for
19 papers
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…
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…
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…
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…