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Found 7 papers, 6 papers with code
Toward Robust Early Detection of Alzheimer's Disease via an Integrated Multimodal Learning Approach
Alzheimer's Disease (AD) is a complex neurodegenerative disorder marked by memory loss, executive dysfunction, and personality changes.
GFE-Mamba: Mamba-based AD Multi-modal Progression Assessment via Generative Feature Extraction from MCI
Alzheimer's Disease (AD) is an irreversible neurodegenerative disorder that often progresses from Mild Cognitive Impairment (MCI), leading to memory loss and significantly impacting patients' lives.
SCKansformer: Fine-Grained Classification of Bone Marrow Cells via Kansformer Backbone and Hierarchical Attention Mechanisms
The model integrates the Kansformer Encoder, SCConv Encoder, and Global-Local Attention Encoder.
RGDAN: A random graph diffusion attention network for traffic prediction
RGDAN comprises a graph diffusion attention module and a temporal attention module.
Ranked #1 on
Traffic Prediction
on NE-BJ
Sepformer-based Models: More Efficient Models for Long Sequence Time-Series Forecasting
These include: 1) complexity - Informer has a relatively high computational complexity and a high memory overhead; 2) nuance - there is limited ability to capture the subtle features in a data stream; 3) interpretability - the inference procedure of Informer is not explainable; 4) extensibility - accuracy is poor with extra-long multivariate time series.
A robust feature reinforcement framework for heterogeneous graphs neural networks
Heterogeneous graph embedding is a crucial step in HGNNs.
CEKD:Cross Ensemble Knowledge Distillation for Augmented Fine-grained Data
We innovatively propose a cross distillation module to provide additional supervision to alleviate the noise problem, and propose a collaborative ensemble module to overcome the target conflict problem.
