Search Results for author:
Found 17 papers, 11 papers with code
Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition
This work explores the challenge of enhancing Automatic Speech Recognition (ASR) model performance across various user-specific domains while preserving user data privacy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations
Brain network analysis is vital for understanding the neural interactions regarding brain structures and functions, and identifying potential biomarkers for clinical phenotypes.
Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM
While there has been a lot of research on representation learning of structured EHR data, the fusion of different types of EHR data (multimodal fusion) is not well studied.
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models
To address this challenge, we delve into synthetic clinical text generation using LLMs for clinical NLP tasks.
Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysis
However, the conventional approach involving static brain network analysis offers limited potential in capturing the dynamism of brain function.
A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises
This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs), including their construction, utilization models, and applications across various healthcare and biomedical research domains.
R-Mixup: Riemannian Mixup for Biological Networks
Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities.
Transformer-Based Hierarchical Clustering for Brain Network Analysis
Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions.
Neighborhood-Regularized Self-Training for Learning with Few Labels
Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36. 8% and saves 57. 3% of the time when compared with the best baseline.
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks
To better adapt GNNs for fMRI analysis, we propose TBDS, an end-to-end framework based on \underline{T}ask-aware \underline{B}rain connectivity \underline{D}AG (short for Directed Acyclic Graph) \underline{S}tructure generation for fMRI analysis.
Brain Network Transformer
Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders.
Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning
Specifically, we propose to meta-train the model on datasets of large sample sizes and transfer the knowledge to small datasets.
FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation
In particular, we formulate (1) prominent region of interest (ROI) features extraction, (2) brain networks generation, and (3) clinical predictions with GNNs, in an end-to-end trainable model under the guidance of particular prediction tasks.
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs.
Effective and Interpretable fMRI Analysis via Functional Brain Network Generation
Recent studies in neuroscience show great potential of functional brain networks constructed from fMRI data for popularity modeling and clinical predictions.
Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integration
Relation prediction among entities in images is an important step in scene graph generation (SGG), which further impacts various visual understanding and reasoning tasks.
Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues
Inspired by the fact that most people carry smart wireless devices with them, e. g. smartphones, we propose to use this wireless identifier as a supervisory label.
