110 citations · 160 across the 15 of their papers we have counts for
15 papers
Nearly Optimal Bayesian Inference for Structural Missingness
Chen Liang, Donghua Yang, Yutong Zhao +9
Structural missingness breaks 'just impute and train': values can be undefined by causal or logical constraints, and the mask may depend on observed variables, unobserved variables…
SAM: A Stability-Aware Cache Manager for Multi-Tenant Embedded Databases
Haoran Zhang, Decheng Zuo, Yu Yan +2
The co-location of multiple database instances on resource constrained edge nodes creates significant cache contention, where traditional schemes are inefficient and unstable under…
KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly Detection
Zhiyu Liang, Dongrui Cai, Chenyuan Zhang +6
Model selection has been raised as an essential problem in the area of time series anomaly detection (TSAD), because there is no single best TSAD model for the highly heterogeneous…
Revisiting Data Analysis with Pre-trained Foundation Models
Chen Liang, Donghua Yang, Zheng Liang +6
Data analysis focuses on harnessing advanced statistics, programming, and machine learning techniques to extract valuable insights from vast datasets. An increasing volume and vari…
An Unsupervised Learning Framework Combined with Heuristics for the Maximum Minimal Cut Problem
Huaiyuan Liu, Xianzhang Liu, Donghua Yang +5
The Maximum Minimal Cut Problem (MMCP), a NP-hard combinatorial optimization (CO) problem, has not received much attention due to the demanding and challenging bi-connectivity cons…
TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis
Zhiyu Liang, Chen Liang, Zheng Liang +2
Unsupervised (a.k.a. Self-supervised) representation learning (URL) has emerged as a new paradigm for time series analysis, because it has the ability to learn generalizable time s…
Unsupervised Multi-modal Feature Alignment for Time Series Representation Learning
Chen Liang, Donghua Yang, Zhiyu Liang +4
In recent times, the field of unsupervised representation learning (URL) for time series data has garnered significant interest due to its remarkable adaptability across diverse do…
A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning
Zhiyu Liang, Jianfeng Zhang, Chen Liang +3
Recent studies have shown great promise in unsupervised representation learning (URL) for multivariate time series, because URL has the capability in learning generalizable represe…
TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification
Huaiyuan Liu, Xianzhang Liu, Donghua Yang +4
Multivariate time series classification (MTSC) is an important data mining task, which can be effectively solved by popular deep learning technology. Unfortunately, the existing de…
UniTS: A Universal Time Series Analysis Framework Powered by Self-Supervised Representation Learning
Zhiyu Liang, Chen Liang, Zheng Liang +2
Machine learning has emerged as a powerful tool for time series analysis. Existing methods are usually customized for different analysis tasks and face challenges in tackling pract…
FedST: Secure Federated Shapelet Transformation for Time Series Classification
Zhiyu Liang, Hongzhi Wang
This paper explores how to build a shapelet-based time series classification (TSC) model in the federated learning (FL) scenario, that is, using more data from multiple owners with…
The Geometry of Nonlinear Embeddings in Kernel Discriminant Analysis
Jiae Kim, Yoonkyung Lee, Zhiyu Liang
Fisher's linear discriminant analysis is a classical method for classification, yet it is limited to capturing linear features only. Kernel discriminant analysis as an extension is…
Low-Power Computer Vision: Status, Challenges, Opportunities
Sergei Alyamkin, Matthew Ardi, Alexander C. Berg +41
Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile…
Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution
Chen Feng, Tao Sheng, Zhiyu Liang +9
The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems wi…
2018 Low-Power Image Recognition Challenge
Sergei Alyamkin, Matthew Ardi, Achille Brighton +38
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing.ieee.org/lpirc) is an annual competition started in 2015. The competition identifies the best technolog…