3 citations · 6 across the 17 of their papers we have counts for
18 papers
Structural-Functional Brain Connectivity Generation via Multimodal Hypergraph-based Flow Matching
Chyong Yi Poh, Hwa Hui Tew, Junn Yong Loo +4
Structural connectivity (SC) and functional connectivity (FC) provide complementary information on interactions between brain regions and are widely used in neuroimaging studies of…
Functional MRI Time Series Generation via Wavelet-Based Image Transform and Spectral Flow Matching for Brain Disorder Identification
Hwa Hui Tew, Junn Yong Loo, Fang Yu Leong +6
Functional Magnetic Resonance Imaging (fMRI) provides non-invasive access to dynamic brain activity by measuring blood oxygen level-dependent (BOLD) signals over time. However, the…
Brain-MGF: Multimodal Graph Fusion Network for EEG-fMRI Brain Connectivity Analysis Under Psilocybin
Sin-Yee Yap, Fuad Noman, Junn Yong Loo +6
Psychedelics, such as psilocybin, reorganise large-scale brain connectivity, yet how these changes are reflected across electrophysiological (electroencephalogram, EEG) and haemody…
T2I-Diff: fMRI Signal Generation via Time-Frequency Image Transform and Classifier-Free Denoising Diffusion Models
Hwa Hui Tew, Junn Yong Loo, Yee-Fan Tan +5
Functional Magnetic Resonance Imaging (fMRI) is an advanced neuroimaging method that enables in-depth analysis of brain activity by measuring dynamic changes in the blood oxygenati…
SpaRTAN: Spatial Reinforcement Token-based Aggregation Network for Visual Recognition
Quan Bi Pay, Vishnu Monn Baskaran, Junn Yong Loo +2
The resurgence of convolutional neural networks (CNNs) in visual recognition tasks, exemplified by ConvNeXt, has demonstrated their capability to rival transformer-based architectu…
Conceptualizing Multi-scale Wavelet Attention and Ray-based Encoding for Human-Object Interaction Detection
Quan Bi Pay, Vishnu Monn Baskaran, Junn Yong Loo +2
Human-object interaction (HOI) detection is essential for accurately localizing and characterizing interactions between humans and objects, providing a comprehensive understanding…
Learning Energy-Based Generative Models via Potential Flow: A Variational Principle Approach to Probability Density Homotopy Matching
Junn Yong Loo, Michelle Adeline, Julia Kaiwen Lau +6
Energy-based models (EBMs) are a powerful class of probabilistic generative models due to their flexibility and interpretability. However, relationships between potential flows and…
ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network for Soft Sensing
Hwa Hui Tew, Fan Ding, Gaoxuan Li +4
Higher-order sensor networks are more accurate in characterizing the nonlinear dynamics of sensory time-series data in modern industrial settings by allowing multi-node connections…
KANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes
Hwa Hui Tew, Gaoxuan Li, Fan Ding +5
Soft sensing of hard-to-measure variables is often crucial in industrial processes. Current practices rely heavily on conventional modeling techniques that show success in improvin…
PKRD-CoT: A Unified Chain-of-thought Prompting for Multi-Modal Large Language Models in Autonomous Driving
Xuewen Luo, Fan Ding, Yinsheng Song +2
There is growing interest in leveraging the capabilities of robust Multi-Modal Large Language Models (MLLMs) directly within autonomous driving contexts. However, the high costs an…
Cross-Domain Transfer Learning using Attention Latent Features for Multi-Agent Trajectory Prediction
Jia Quan Loh, Xuewen Luo, Fan Ding +5
With the advancements of sensor hardware, traffic infrastructure and deep learning architectures, trajectory prediction of vehicles has established a solid foundation in intelligen…
Energy-efficient Hybrid Model Predictive Trajectory Planning for Autonomous Electric Vehicles
Fan Ding, Xuewen Luo, Gaoxuan Li +6
To tackle the twin challenges of limited battery life and lengthy charging durations in electric vehicles (EVs), this paper introduces an Energy-efficient Hybrid Model Predictive P…
Variational Potential Flow: A Novel Probabilistic Framework for Energy-Based Generative Modelling
Junn Yong Loo, Michelle Adeline, Arghya Pal +3
Energy based models (EBMs) are appealing for their generality and simplicity in data likelihood modeling, but have conventionally been difficult to train due to the unstable and ti…
MDHA: Multi-Scale Deformable Transformer with Hybrid Anchors for Multi-View 3D Object Detection
Michelle Adeline, Junn Yong Loo, Vishnu Monn Baskaran
Multi-view 3D object detection is a crucial component of autonomous driving systems. Contemporary query-based methods primarily depend either on dataset-specific initialization of…
Sigma-point Kalman Filter with Nonlinear Unknown Input Estimation via Optimization and Data-driven Approach for Dynamic Systems
Junn Yong Loo, Ze Yang Ding, Vishnu Monn Baskaran +2
Most works on joint state and unknown input (UI) estimation require the assumption that the UIs are linear; this is potentially restrictive as it does not hold in many intelligent…
A Deep Probabilistic Flow-Based Framework for Unsupervised Cross-Domain Soft Sensing
Junn Yong Loo, Hwa Hui Tew, Fang Yu Leong +4
Industrial soft sensing is crucial for accurate process monitoring through reliable inference of dominant sensor variables. However, developing effective data-driven soft sensor mo…
Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes Framework
Shageenderan Sapai, Junn Yong Loo, Ze Yang Ding +4
Recently, data-driven models such as deep neural networks have shown to be promising tools for modelling and state inference in soft robots. However, voluminous amounts of data are…
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification
Sin-Yee Yap, Junn Yong Loo, Chee-Ming Ting +4
Recent applications of pattern recognition techniques on brain connectome classification using functional connectivity (FC) are shifting towards acknowledging the non-Euclidean top…