18 citations · 41 across the 38 of their papers we have counts for
12 papers · 1 filter
ALScope: A Unified Toolkit for Deep Active Learning
Chenkai Wu, Yuanyuan Qi, Xiaohao Yang +4
Deep Active Learning (DAL) reduces annotation costs by selecting the most informative unlabeled samples during training. As real-world applications become more complex, challenges…
MoTime: A Dataset Suite for Multimodal Time Series Forecasting
Xin Zhou, Weiqing Wang, Francisco J. Baldán +2
While multimodal data sources are increasingly available from real-world forecasting, most existing research remains on unimodal time series. In this work, we present MoTime, a sui…
DPL: Decoupled Prototype Learning for Enhancing Robustness of Vision-Language Transformers to Missing Modalities
Jueqing Lu, Yuanyuan Qi, Xiaohao Yang +8
The performance of Visio-Language Transformers drops sharply when an input modality (e.g., image) is missing, because the model is forced to make predictions using incomplete infor…
Scalable Transformer for High Dimensional Multivariate Time Series Forecasting
Xin Zhou, Weiqing Wang, Wray Buntine +4
Deep models for Multivariate Time Series (MTS) forecasting have recently demonstrated significant success. Channel-dependent models capture complex dependencies that channel-indepe…
Navigating Conflicting Views: Harnessing Trust for Learning
Jueqing Lu, Wray Buntine, Yuanyuan Qi +3
Resolving conflicts is critical for improving the reliability of multi-view classification. While prior work focuses on learning consistent and informative representations across v…
OntoMedRec: Logically-Pretrained Model-Agnostic Ontology Encoders for Medication Recommendation
Weicong Tan, Weiqing Wang, Xin Zhou +3
Most existing medication recommendation models learn representations for medical concepts based on electronic health records (EHRs) and make recommendations with learnt representat…
Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification
Wei Tan, Ngoc Dang Nguyen, Lan Du +1
Within the scope of natural language processing, the domain of multi-label text classification is uniquely challenging due to its expansive and uneven label distribution. The compl…
Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning
Wei Tan, Lan Du, Wray Buntine
The effectiveness of active learning largely depends on the sampling efficiency of the acquisition function. Expected Loss Reduction (ELR) focuses on a Bayesian estimate of the red…
Open-Set Graph Anomaly Detection via Normal Structure Regularisation
Qizhou Wang, Guansong Pang, Mahsa Salehi +2
This paper considers an important Graph Anomaly Detection (GAD) task, namely open-set GAD, which aims to train a detection model using a small number of normal and anomaly nodes (r…
Topic Modelling Meets Deep Neural Networks: A Survey
He Zhao, Dinh Phung, Viet Huynh +3
Topic modelling has been a successful technique for text analysis for almost twenty years. When topic modelling met deep neural networks, there emerged a new and increasingly popul…
Temporal Cascade and Structural Modelling of EHRs for Granular Readmission Prediction
Bhagya Hettige, Weiqing Wang, Yuan-Fang Li +2
Predicting (1) when the next hospital admission occurs and (2) what will happen in the next admission about a patient by mining electronic health record (EHR) data can provide gran…
Discriminative, Generative and Self-Supervised Approaches for Target-Agnostic Learning
Yuan Jin, Wray Buntine, Francois Petitjean +1
Supervised learning, characterized by both discriminative and generative learning, seeks to predict the values of single (or sometimes multiple) predefined target attributes based…