105 citations · 372 across the 39 of their papers we have counts for
39 papers
Deep Generation of Heterogeneous Networks
Chen Ling, Carl Yang, Liang Zhao
Heterogeneous graphs are ubiquitous data structures that can inherently capture multi-type and multi-modal interactions between objects. In recent years, research on encoding heter…
A Convergent ADMM Framework for Efficient Neural Network Training
Junxiang Wang, Hongyi Li, Liang Zhao
As a well-known optimization framework, the Alternating Direction Method of Multipliers (ADMM) has achieved tremendous success in many classification and regression applications. R…
KNAS: Green Neural Architecture Search
Jingjing Xu, Liang Zhao, Junyang Lin +3
Many existing neural architecture search (NAS) solutions rely on downstream training for architecture evaluation, which takes enormous computations. Considering that these computat…
Long-term, Short-term and Sudden Event: Trading Volume Movement Prediction with Graph-based Multi-view Modeling
Liang Zhao, Wei Li, Ruihan Bao +3
Trading volume movement prediction is the key in a variety of financial applications. Despite its importance, there is few research on this topic because of its requirement for com…
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
Zhiqian Chen, Fanglan Chen, Lei Zhang +7
Deep learning's performance has been extensively recognized recently. Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does…
Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning
Yuyang Gao, Giorgio A. Ascoli, Liang Zhao
Continual learning is considered a promising step towards next-generation Artificial Intelligence (AI), where deep neural networks (DNNs) make decisions by continuously learning a…
Interpretable Distance Metric Learning for Handwritten Chinese Character Recognition
Boxiang Dong, Aparna S. Varde, Danilo Stevanovic +2
Handwriting recognition is of crucial importance to both Human Computer Interaction (HCI) and paperwork digitization. In the general field of Optical Character Recognition (OCR), h…
Cyber Intrusion Detection by Using Deep Neural Networks with Attack-sharing Loss
Boxiang Dong, Hui, Wang +5
Cyber attacks pose crucial threats to computer system security, and put digital treasuries at excessive risks. This leads to an urgent call for an effective intrusion detection sys…
Sign-regularized Multi-task Learning
Johnny Torres, Guangji Bai, Junxiang Wang +3
Multi-task learning is a framework that enforces different learning tasks to share their knowledge to improve their generalization performance. It is a hot and active domain that s…
GP: Context-free Grammar Pre-training for Text-to-SQL Parsers
Liang Zhao, Hexin Cao, Yunsong Zhao
A new method for Text-to-SQL parsing, Grammar Pre-training (GP), is proposed to decode deep relations between question and database. Firstly, to better utilize the information of d…
Disentangled Dynamic Graph Deep Generation
Wenbin Zhang, Liming Zhang, Dieter Pfoser +1
Deep generative models for graphs have exhibited promising performance in ever-increasing domains such as design of molecules (i.e, graph of atoms) and structure prediction of prot…
Graph-based Multi-hop Reasoning for Long Text Generation
Liang Zhao, Jingjing Xu, Junyang Lin +3
Long text generation is an important but challenging task.The main problem lies in learning sentence-level semantic dependencies which traditional generative models often suffer fr…
Factorized Deep Generative Models for Trajectory Generation with Spatiotemporal-Validity Constraints
Liming Zhang, Liang Zhao, Dieter Pfoser
Trajectory data generation is an important domain that characterizes the generative process of mobility data. Traditional methods heavily rely on predefined heuristics and distribu…
Tunable Subnetwork Splitting for Model-parallelism of Neural Network Training
Junxiang Wang, Zheng Chai, Yue Cheng +1
Alternating minimization methods have recently been proposed as alternatives to the gradient descent for deep neural network optimization. Alternating minimization methods can typi…
Toward Model Parallelism for Deep Neural Network based on Gradient-free ADMM Framework
Junxiang Wang, Zheng Chai, Yue Cheng +1
Alternating Direction Method of Multipliers (ADMM) has recently been proposed as a potential alternative optimizer to the Stochastic Gradient Descent(SGD) for deep learning problem…
A Systematic Survey on Deep Generative Models for Graph Generation
Xiaojie Guo, Liang Zhao
Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in…
TG-GAN: Continuous-time Temporal Graph Generation with Deep Generative Models
Liming Zhang, Liang Zhao, Shan Qin +1
The recent deep generative models for static graphs that are now being actively developed have achieved significant success in areas such as molecule design. However, many real-wor…
Generating Tertiary Protein Structures via an Interpretative Variational Autoencoder
Xiaojie Guo, Yuanqi Du, Sivani Tadepalli +2
Much scientific enquiry across disciplines is founded upon a mechanistic treatment of dynamic systems that ties form to function. A highly visible instance of this is in molecular…
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks
Zhiqian Chen, Fanglan Chen, Lei Zhang +7
Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing. As an…
Learning to Recommend via Meta Parameter Partition
Liang Zhao, Yang Wang, Daxiang Dong +1
In this paper we propose to solve an important problem in recommendation -- user cold start, based on meta leaning method. Previous meta learning approaches finetune all parameters…
Compositional Generalization for Primitive Substitutions
Yuanpeng Li, Liang Zhao, Jianyu Wang +1
Compositional generalization is a basic mechanism in human language learning, but current neural networks lack such ability. In this paper, we conduct fundamental research for enco…
BEAN: Interpretable Representation Learning with Biologically-Enhanced Artificial Neuronal Assembly Regularization
Yuyang Gao, Giorgio A. Ascoli, Liang Zhao
Deep neural networks (DNNs) are known for extracting useful information from large amounts of data. However, the representations learned in DNNs are typically hard to interpret, es…
Interpreting and Evaluating Neural Network Robustness
Fuxun Yu, Zhuwei Qin, Chenchen Liu +3
Recently, adversarial deception becomes one of the most considerable threats to deep neural networks. However, compared to extensive research in new designs of various adversarial…
Learning Good Representation via Continuous Attention
Liang Zhao, Wei Xu
In this paper we present our scientific discovery that good representation can be learned via continuous attention during the interaction between Unsupervised Learning(UL) and Rein…