33 citations · 100 across the 12 of their papers we have counts for
13 papers
Towards Label Position Bias in Graph Neural Networks
Haoyu Han, Xiaorui Liu, Feng Shi +3
Graph Neural Networks (GNNs) have emerged as a powerful tool for semi-supervised node classification tasks. However, recent studies have revealed various biases in GNNs stemming fr…
Alternately Optimized Graph Neural Networks
Haoyu Han, Xiaorui Liu, Haitao Mao +4
Graph Neural Networks (GNNs) have greatly advanced the semi-supervised node classification task on graphs. The majority of existing GNNs are trained in an end-to-end manner that ca…
Learning from the Tangram to Solve Mini Visual Tasks
Yizhou Zhao, Liang Qiu, Pan Lu +3
Current pre-training methods in computer vision focus on natural images in the daily-life context. However, abstract diagrams such as icons and symbols are common and important in…
GenMotion: Data-driven Motion Generators for Real-time Animation Synthesis
Yizhou Zhao, Wensi Ai, Liang Qiu +4
With the recent success of deep learning algorithms, many researchers have focused on generative models for human motion animation. However, the research community lacks a platform…
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…
HUGE2: a Highly Untangled Generative-model Engine for Edge-computing
Feng Shi, Ziheng Xu, Tao Yuan +1
As a type of prominent studies in deep learning, generative models have been widely investigated in research recently. Two research branches of the deep learning models, the Genera…
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…
Sparse Winograd Convolutional neural networks on small-scale systolic arrays
Feng Shi, Haochen Li, Yuhe Gao +2
The reconfigurability, energy-efficiency, and massive parallelism on FPGAs make them one of the best choices for implementing efficient deep learning accelerators. However, state-o…
Interpreting CNN Knowledge via an Explanatory Graph
Quanshi Zhang, Ruiming Cao, Feng Shi +2
This paper learns a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside a pre-trained CNN. Considering that each filter in a conv-laye…