8 citations · 18 across the 8 of their papers we have counts for
8 papers
Fine-Grained Traceability for Transparent ML Pipelines
Liping Chen, Mujie Liu, Haytham Fayek
Modern machine learning systems are increasingly realised as multistage pipelines, yet existing transparency mechanisms typically operate at a model level: they describe what a sys…
Structure Matters: Brain Graph Augmentation via Learnable Edge Masking for Data-efficient Psychiatric Diagnosis
Mujie Liu, Chenze Wang, Liping Chen +5
The limited availability of labeled brain network data makes it challenging to achieve accurate and interpretable psychiatric diagnoses. While self-supervised learning (SSL) offers…
Explainable Graph Neural Networks: Understanding Brain Connectivity and Biomarkers in Dementia
Niharika Tewari, Nguyen Linh Dan Le, Mujie Liu +5
Dementia is a progressive neurodegenerative disorder with multiple etiologies, including Alzheimer's disease, Parkinson's disease, frontotemporal dementia, and vascular dementia. I…
Data-Efficient Psychiatric Disorder Detection via Self-supervised Learning on Frequency-enhanced Brain Networks
Mujie Liu, Mengchu Zhu, Qichao Dong +4
Psychiatric disorders involve complex neural activity changes, with functional magnetic resonance imaging (fMRI) data serving as key diagnostic evidence. However, data scarcity and…
Causal Prompting for Implicit Sentiment Analysis with Large Language Models
Jing Ren, Wenhao Zhou, Bowen Li +7
Implicit Sentiment Analysis (ISA) aims to infer sentiment that is implied rather than explicitly stated, requiring models to perform deeper reasoning over subtle contextual cues. W…
Balanced Graph Structure Information for Brain Disease Detection
Falih Gozi Febrinanto, Mujie Liu, Feng Xia
Analyzing connections between brain regions of interest (ROI) is vital to detect neurological disorders such as autism or schizophrenia. Recent advancements employ graph neural net…
Entropy Causal Graphs for Multivariate Time Series Anomaly Detection
Falih Gozi Febrinanto, Kristen Moore, Chandra Thapa +4
Many multivariate time series anomaly detection frameworks have been proposed and widely applied. However, most of these frameworks do not consider intrinsic relationships between…
Coupled Attention Networks for Multivariate Time Series Anomaly Detection
Feng Xia, Xin Chen, Shuo Yu +3
Multivariate time series anomaly detection (MTAD) plays a vital role in a wide variety of real-world application domains. Over the past few years, MTAD has attracted rapidly increa…