Search Results for author:
Found 15 papers, 11 papers with code
$α$-GAN by Rényi Cross Entropy
The value function is formulated, by R\'{e}nyi cross entropy, as an expected certainty measure incurred by the discriminator's soft decision as to where the sample is from, true population or the generator.
BrainPrompt: Multi-Level Brain Prompt Enhancement for Neurological Condition Identification
In this paper, we present BrainPrompt, an innovative framework that enhances Graph Neural Networks (GNNs) by integrating Large Language Models (LLMs) with knowledge-driven prompts, enabling more effective capture of complex, non-imaging information and external knowledge for neurological disease identification.
BrainOOD: Out-of-distribution Generalizable Brain Network Analysis
Graph Neural Networks (GNNs) have shown promising in analyzing brain networks, but there are two major challenges in using GNNs: (1) distribution shifts in multi-site brain network data, leading to poor Out-of-Distribution (OOD) generalization, and (2) limited interpretability in identifying key brain regions critical to neurological disorders.
ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation
Another challenge lies in aligning the domain-specific and cross-domain sequences.
Text4Seg: Reimagining Image Segmentation as Text Generation
Multimodal Large Language Models (MLLMs) have shown exceptional capabilities in vision-language tasks; however, effectively integrating image segmentation into these models remains a significant challenge.
Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion
Some recent methods have proposed utilizing multiple atlases, but they neglect consistency across atlases and lack ROI-level information exchange.
Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification
It generates a prior-knowledge-enhanced contrast graph to address the distribution shifts across sub-populations by a two-stream attention mechanism.
SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation
Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation.
CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task Learning
To dually improve the performance of temporal states evolution and incremental recommendation, we design a Pseudo-Multi-Task Learning (PMTL) paradigm by stacking the incremental single-target recommendations into one multi-target task for joint optimization.
Contrastive Graph Pooling for Explainable Classification of Brain Networks
Our contributions underscore the potential of ContrastPool for advancing the understanding of brain networks and neurodegenerative conditions.
Union Subgraph Neural Networks
We first investigate different kinds of connectivities existing in a local neighborhood and identify a substructure called union subgraph, which is able to capture the complete picture of the 1-hop neighborhood of an edge.
IMF: Interactive Multimodal Fusion Model for Link Prediction
To this end, we propose a two-stage multimodal fusion framework to preserve modality-specific knowledge as well as take advantage of the complementarity between different modalities.
Data-Driven Network Neuroscience: On Data Collection and Benchmark
This paper presents a comprehensive and quality collection of functional human brain network data for potential research in the intersection of neuroscience, machine learning, and graph analytics.
Entity Aware Syntax Tree Based Data Augmentation for Natural Language Understanding
One of the main challenges is to collect a sufficient amount of annotated data to train a model.
A Class-Aware Representation Refinement Framework for Graph Classification
In this paper, we propose a Class-Aware Representation rEfinement (CARE) framework for the task of graph classification.
