193 citations · 386 across the 31 of their papers we have counts for
12 papers · 1 filter
CktGNN: Circuit Graph Neural Network for Electronic Design Automation
Zehao Dong, Weidong Cao, Muhan Zhang +3
The electronic design automation of analog circuits has been a longstanding challenge in the integrated circuit field due to the huge design space and complex design trade-offs amo…
Understanding Deep Neural Networks via Linear Separability of Hidden Layers
Chao Zhang, Xinyu Chen, Wensheng Li +3
In this paper, we measure the linear separability of hidden layer outputs to study the characteristics of deep neural networks. In particular, we first propose Minkowski difference…
Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
Jiarui Feng, Lecheng Kong, Hao Liu +4
Message passing neural networks (MPNNs) have emerged as the most popular framework of graph neural networks (GNNs) in recent years. However, their expressive power is limited by th…
AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data
Hao Wang, Lixue Liu, Xueguan Song +2
In tunnel boring machine (TBM) underground projects, an accurate description of the rock-soil types distributed in the tunnel can decrease the construction risk ({\it e.g.} surface…
Alternating Differentiation for Optimization Layers
Haixiang Sun, Ye Shi, Jingya Wang +3
The idea of embedding optimization problems into deep neural networks as optimization layers to encode constraints and inductive priors has taken hold in recent years. Most existin…
Exploring the Relationship between Architecture and Adversarially Robust Generalization
Aishan Liu, Shiyu Tang, Siyuan Liang +4
Adversarial training has been demonstrated to be one of the most effective remedies for defending adversarial examples, yet it often suffers from the huge robustness generalization…
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning
Jixian Guo, Mingming Gong, Dacheng Tao
The generalization of model-based reinforcement learning (MBRL) methods to environments with unseen transition dynamics is an important yet challenging problem. Existing methods tr…
Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method
Wensheng Li, Chao Zhang, Chuncheng Wang +2
Physics-informed neural networks (PINNs) provide a deep learning framework for numerically solving partial differential equations (PDEs), and have been widely used in a variety of…
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?
Yibo Yang, Shixiang Chen, Xiangtai Li +3
Modern deep neural networks for classification usually jointly learn a backbone for representation and a linear classifier to output the logit of each class. A recent study has sho…
GAN-MDF: A Method for Multi-fidelity Data Fusion in Digital Twins
Lixue Liu, Chao Zhang, Dacheng Tao
The Internet of Things (IoT) collects real-time data of physical systems, such as smart factory, intelligent robot and healtcare system, and provide necessary support for digital t…
Training Robust Deep Neural Networks via Adversarial Noise Propagation
Aishan Liu, Xianglong Liu, Chongzhi Zhang +3
In practice, deep neural networks have been found to be vulnerable to various types of noise, such as adversarial examples and corruption. Various adversarial defense methods have…
Investigation of wind pressures on tall building under interference effects using machine learning techniques
Gang Hu, Lingbo Liu, Dacheng Tao +2
Interference effects of tall buildings have attracted numerous studies due to the boom of clusters of tall buildings in megacities. To fully understand the interference effects of…