7 citations · 10 across the 6 of their papers we have counts for
6 papers
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…
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…
Refined Causal Graph Structure Learning via Curvature for Brain Disease Classification
Falih Gozi Febrinanto, Adonia Simango, Chengpei Xu +4
Graph neural networks (GNNs) have been developed to model the relationship between regions of interest (ROIs) in brains and have shown significant improvement in detecting brain di…
Rehearsal with Auxiliary-Informed Sampling for Audio Deepfake Detection
Falih Gozi Febrinanto, Kristen Moore, Chandra Thapa +3
The performance of existing audio deepfake detection frameworks degrades when confronted with new deepfake attacks. Rehearsal-based continual learning (CL), which updates models us…
SIGNL: A Label-Efficient Audio Deepfake Detection System via Spectral-Temporal Graph Non-Contrastive Learning
Falih Gozi Febrinanto, Kristen Moore, Chandra Thapa +2
Audio deepfake detection is increasingly important as synthetic speech becomes more realistic and accessible. Recent methods, including those using graph neural networks (GNNs) to…
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…