57 citations · 115 across the 12 of their papers we have counts for
12 papers
DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection
Lixu Wang, Shichao Xu, Xinyu Du +1
Anomaly detection in time-series data is crucial for identifying faults, failures, threats, and outliers across a range of applications. Recently, deep learning techniques have bee…
Open Vocabulary Multi-Label Classification with Dual-Modal Decoder on Aligned Visual-Textual Features
Shichao Xu, Yikang Li, Jenhao Hsiao +2
In computer vision, multi-label recognition are important tasks with many real-world applications, but classifying previously unseen labels remains a significant challenge. In this…
Federated Class-Incremental Learning
Jiahua Dong, Lixu Wang, Zhen Fang +4
Federated learning (FL) has attracted growing attention via data-private collaborative training on decentralized clients. However, most existing methods unrealistically assume obje…
Learning-based Framework for Sensor Fault-Tolerant Building HVAC Control with Model-assisted Learning
Shichao Xu, Yangyang Fu, Yixuan Wang +2
As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort a…
Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization
Lixu Wang, Shichao Xu, Ruiqi Xu +2
As Artificial Intelligence as a Service gains popularity, protecting well-trained models as intellectual property is becoming increasingly important. There are two common types of…
Cocktail: Learn a Better Neural Network Controller from Multiple Experts via Adaptive Mixing and Robust Distillation
Yixuan Wang, Chao Huang, Zhilu Wang +3
Neural networks are being increasingly applied to control and decision-making for learning-enabled cyber-physical systems (LE-CPSs). They have shown promising performance without r…
Weak Adaptation Learning -- Addressing Cross-domain Data Insufficiency with Weak Annotator
Shichao Xu, Lixu Wang, Yixuan Wang +1
Data quantity and quality are crucial factors for data-driven learning methods. In some target problem domains, there are not many data samples available, which could significantly…
Addressing Class Imbalance in Federated Learning
Lixu Wang, Shichao Xu, Xiao Wang +1
Federated learning (FL) is a promising approach for training decentralized data located on local client devices while improving efficiency and privacy. However, the distribution an…
One for Many: Transfer Learning for Building HVAC Control
Shichao Xu, Yixuan Wang, Yanzhi Wang +2
The design of building heating, ventilation, and air conditioning (HVAC) system is critically important, as it accounts for around half of building energy consumption and directly…
Opportunistic Intermittent Control with Safety Guarantees for Autonomous Systems
Chao Huang, Shichao Xu, Zhilu Wang +3
Control schemes for autonomous systems are often designed in a way that anticipates the worst case in any situation. At runtime, however, there could exist opportunities to leverag…
Eavesdrop the Composition Proportion of Training Labels in Federated Learning
Lixu Wang, Shichao Xu, Xiao Wang +1
Federated learning (FL) has recently emerged as a new form of collaborative machine learning, where a common model can be learned while keeping all the training data on local devic…
MaskPlus: Improving Mask Generation for Instance Segmentation
Shichao Xu, Shuyue Lan, Qi Zhu
Instance segmentation is a promising yet challenging topic in computer vision. Recent approaches such as Mask R-CNN typically divide this problem into two parts -- a detection comp…