Search Results for author:
Found 12 papers, 5 papers with code
Dual-Scale Transformer for Large-Scale Single-Pixel Imaging
In this paper, we propose a deep unfolding network with hybrid-attention Transformer on Kronecker SPI model, dubbed HATNet, to improve the imaging quality of real SPI cameras.
An Interpretable Cross-Attentive Multi-modal MRI Fusion Framework for Schizophrenia Diagnosis
Both functional and structural magnetic resonance imaging (fMRI and sMRI) are widely used for the diagnosis of mental disorder.
LLM4SecHW: Leveraging Domain Specific Large Language Model for Hardware Debugging
To address these challenges, we propose a unique approach to compile a dataset of open source hardware design defects and their remediation steps, utilizing version control data.
Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies
Functional connectivity (FC) as derived from fMRI has emerged as a pivotal tool in elucidating the intricacies of various psychiatric disorders and delineating the neural pathways that underpin cognitive and behavioral dynamics inherent to the human brain.
Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results
Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10, 000 training subjects without double-dipping.
Angle Basis: a Generative Model and Decomposition for Functional Connectivity
We also find that both the decomposition and its residual have approximately equal predictive value, and when combined into an ensemble, exceed the AUC of FC-based prediction by up to 5%.
Latent Similarity Identifies Important Functional Connections for Phenotype Prediction
Significance: We propose a novel algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data.
Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systems
From the view of object detectors, the traffic sign`s position and quality of the video are continuously changing, rendering the digital AEs ineffective in the physical world.
Don't Forget to Sign the Gradients!
Engineering a top-notch deep learning model is an expensive procedure that involves collecting data, hiring human resources with expertise in machine learning, and providing high computational resources.
Meta Federated Learning
Due to its distributed methodology alongside its privacy-preserving features, Federated Learning (FL) is vulnerable to training time adversarial attacks.
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction
Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating the fMRI time series and the functional connectivity (FC) between each pair of brain regions.
Distance Correlation Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies
Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity patterns have been extensively utilized to delineate global functional organization of the human brain in health, development, and neuropsychiatric disorders.
