87 citations · 350 across the 30 of their papers we have counts for
12 papers · 1 filter
QLASS: Boosting Language Agent Inference via Q-Guided Stepwise Search
Zongyu Lin, Yao Tang, Xingcheng Yao +4
Language agents have become a promising solution to complex interactive tasks. One of the key ingredients to the success of language agents is the reward model on the trajectory of…
Cross-Modality Program Representation Learning for Electronic Design Automation with High-Level Synthesis
Zongyue Qin, Yunsheng Bai, Atefeh Sohrabizadeh +4
In recent years, domain-specific accelerators (DSAs) have gained popularity for applications such as deep learning and autonomous driving. To facilitate DSA designs, programmers us…
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…
ProgSG: Cross-Modality Representation Learning for Programs in Electronic Design Automation
Yunsheng Bai, Atefeh Sohrabizadeh, Zongyue Qin +3
Recent years have witnessed the growing popularity of domain-specific accelerators (DSAs), such as Google's TPUs, for accelerating various applications such as deep learning, searc…
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…
Improving Multi-Task Generalization via Regularizing Spurious Correlation
Ziniu Hu, Zhe Zhao, Xinyang Yi +4
Multi-Task Learning (MTL) is a powerful learning paradigm to improve generalization performance via knowledge sharing. However, existing studies find that MTL could sometimes hurt…
Fuzzy Logic Based Logical Query Answering on Knowledge Graphs
Xuelu Chen, Ziniu Hu, Yizhou Sun
Answering complex First-Order Logical (FOL) queries on large-scale incomplete knowledge graphs (KGs) is an important yet challenging task. Recent advances embed logical queries and…
Motif-Driven Contrastive Learning of Graph Representations
Shichang Zhang, Ziniu Hu, Arjun Subramonian +1
Pre-training Graph Neural Networks (GNN) via self-supervised contrastive learning has recently drawn lots of attention. However, most existing works focus on node-level contrastive…
GPT-GNN: Generative Pre-Training of Graph Neural Networks
Ziniu Hu, Yuxiao Dong, Kuansan Wang +2
Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data. However, training GNNs usually requires abundant task-specific labeled data, w…
Heterogeneous Graph Transformer
Ziniu Hu, Yuxiao Dong, Kuansan Wang +1
Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data. However, most GNNs are designed for homogeneous graphs, in which all…
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Difan Zou, Ziniu Hu, Yewen Wang +3
Graph convolutional networks (GCNs) have recently received wide attentions, due to their successful applications in different graph tasks and different domains. Training GCNs for a…
Pre-Training Graph Neural Networks for Generic Structural Feature Extraction
Ziniu Hu, Changjun Fan, Ting Chen +2
Graph neural networks (GNNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GNN model requires a large collection of label…