247 citations · 762 across the 30 of their papers we have counts for
30 papers
FITS: Modeling Time Series with Parameters
Zhijian Xu, Ailing Zeng, Qiang Xu
In this paper, we introduce FITS, a lightweight yet powerful model for time series analysis. Unlike existing models that directly process raw time-domain data, FITS operates on the…
DeepGate2: Functionality-Aware Circuit Representation Learning
Zhengyuan Shi, Hongyang Pan, Sadaf Khan +7
Circuit representation learning aims to obtain neural representations of circuit elements and has emerged as a promising research direction that can be applied to various EDA and l…
FrAug: Frequency Domain Augmentation for Time Series Forecasting
Muxi Chen, Zhijian Xu, Ailing Zeng +1
Data augmentation (DA) has become a de facto solution to expand training data size for deep learning. With the proliferation of deep models for time series analysis, various time s…
DeepSeq: Deep Sequential Circuit Learning
Sadaf Khan, Zhengyuan Shi, Min Li +1
Circuit representation learning is a promising research direction in the electronic design automation (EDA) field. With sufficient data for pre-training, the learned general yet ef…
SATformer: Transformer-Based UNSAT Core Learning
Zhengyuan Shi, Min Li, Yi Liu +5
This paper introduces SATformer, a novel Transformer-based approach for the Boolean Satisfiability (SAT) problem. Rather than solving the problem directly, SATformer approaches the…
Be Your Own Neighborhood: Detecting Adversarial Example by the Neighborhood Relations Built on Self-Supervised Learning
Zhiyuan He, Yijun Yang, Pin-Yu Chen +2
Deep Neural Networks (DNNs) have achieved excellent performance in various fields. However, DNNs' vulnerability to Adversarial Examples (AE) hinders their deployments to safety-cri…
DeepTPI: Test Point Insertion with Deep Reinforcement Learning
Zhengyuan Shi, Min Li, Sadaf Khan +4
Test point insertion (TPI) is a widely used technique for testability enhancement, especially for logic built-in self-test (LBIST) due to its relatively low fault coverage. In this…
DeepSAT: An EDA-Driven Learning Framework for SAT
Min Li, Zhengyuan Shi, Qiuxia Lai +3
We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. Unlike existing solutions trained on random SAT instances with relatively we…
Are Transformers Effective for Time Series Forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang +1
Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we q…
What You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradiction
Yijun Yang, Ruiyuan Gao, Yu Li +2
Adversarial examples (AEs) pose severe threats to the applications of deep neural networks (DNNs) to safety-critical domains, e.g., autonomous driving. While there has been a vast…
DeepFIB: Self-Imputation for Time Series Anomaly Detection
Minhao Liu, Zhijian Xu, Qiang Xu
Time series (TS) anomaly detection (AD) plays an essential role in various applications, e.g., fraud detection in finance and healthcare monitoring. Due to the inherently unpredict…
SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos
Ailing Zeng, Lei Yang, Xuan Ju +3
When analyzing human motion videos, the output jitters from existing pose estimators are highly-unbalanced with varied estimation errors across frames. Most frames in a video are r…
Testability-Aware Low Power Controller Design with Evolutionary Learning
Min Li, Zhengyuan Shi, Zezhong Wang +3
XORNet-based low power controller is a popular technique to reduce circuit transitions in scan-based testing. However, existing solutions construct the XORNet evenly for scan chain…
DeepGate: Learning Neural Representations of Logic Gates
Min Li, Sadaf Khan, Zhengyuan Shi +3
Applying deep learning (DL) techniques in the electronic design automation (EDA) field has become a trending topic. Most solutions apply well-developed DL models to solve specific…
Learning Skeletal Graph Neural Networks for Hard 3D Pose Estimation
Ailing Zeng, Xiao Sun, Lei Yang +3
Various deep learning techniques have been proposed to solve the single-view 2D-to-3D pose estimation problem. While the average prediction accuracy has been improved significantly…
Information Bottleneck Approach to Spatial Attention Learning
Qiuxia Lai, Yu Li, Ailing Zeng +3
The selective visual attention mechanism in the human visual system (HVS) restricts the amount of information to reach visual awareness for perceiving natural scenes, allowing near…
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction
Minhao Liu, Ailing Zeng, Muxi Chen +4
One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences. By taking advantage of this property, we propose…
TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks
Yu Li, Min Li, Qiuxia Lai +2
Deep learning (DL) has achieved unprecedented success in a variety of tasks. However, DL systems are notoriously difficult to test and debug due to the lack of explainability of DL…
Relational Graph Neural Network Design via Progressive Neural Architecture Search
Ailing Zeng, Minhao Liu, Zhiwei Liu +3
We propose a novel solution to addressing a long-standing dilemma in the representation learning of graph neural networks (GNNs): how to effectively capture and represent useful in…
AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference
Min Li, Yu Li, Ye Tian +2
This paper presents AppealNet, a novel edge/cloud collaborative architecture that runs deep learning (DL) tasks more efficiently than state-of-the-art solutions. For a given input,…
Skimming and Scanning for Untrimmed Video Action Recognition
Yunyan Hong, Ailing Zeng, Min Li +3
Video action recognition (VAR) is a primary task of video understanding, and untrimmed videos are more common in real-life scenes. Untrimmed videos have redundant and diverse clips…
MixDefense: A Defense-in-Depth Framework for Adversarial Example Detection Based on Statistical and Semantic Analysis
Yijun Yang, Ruiyuan Gao, Yu Li +2
Machine learning with deep neural networks (DNNs) has become one of the foundation techniques in many safety-critical systems, such as autonomous vehicles and medical diagnosis sys…
T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis
Minhao Liu, Ailing Zeng, Qiuxia Lai +1
Sensor-based time series analysis is an essential task for applications such as activity recognition and brain-computer interface. Recently, features extracted with deep neural net…
DeepDyve: Dynamic Verification for Deep Neural Networks
Yu Li, Min Li, Bo Luo +2
Deep neural networks (DNNs) have become one of the enabling technologies in many safety-critical applications, e.g., autonomous driving and medical image analysis. DNN systems, how…