33 citations · 60 across the 5 of their papers we have counts for
8 papers
Transformer-based Machine Learning for Fast SAT Solvers and Logic Synthesis
Feng Shi, Chonghan Lee, Mohammad Khairul Bashar +3
CNF-based SAT and MaxSAT solvers are central to logic synthesis and verification systems. The increasing popularity of these constraint problems in electronic design automation enc…
STAR: Sparse Transformer-based Action Recognition
Feng Shi, Chonghan Lee, Liang Qiu +6
The cognitive system for human action and behavior has evolved into a deep learning regime, and especially the advent of Graph Convolution Networks has transformed the field in rec…
VersaGNN: a Versatile accelerator for Graph neural networks
Feng Shi, Ahren Yiqiao Jin, Song-Chun Zhu
\textit{Graph Neural Network} (GNN) is a promising approach for analyzing graph-structured data that tactfully captures their dependency information via node-level message passing.…
Vertical-Horizontal Structured Attention for Generating Music with Chords
Yizhou Zhao, Liang Qiu, Wensi Ai +2
In this paper, we propose a lightweight music-generating model based on variational autoencoder (VAE) with structured attention. Generating music is different from generating text…
Structured Attention for Unsupervised Dialogue Structure Induction
Liang Qiu, Yizhou Zhao, Weiyan Shi +5
Inducing a meaningful structural representation from one or a set of dialogues is a crucial but challenging task in computational linguistics. Advancement made in this area is crit…
Explanatory Graphs for CNNs
Quanshi Zhang, Xin Wang, Ruiming Cao +3
This paper introduces a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside conv-layers of a pre-trained CNN. Each filter in a conv-la…