Image compression has made significant progress through end-to-end deep-learning approaches in recent years. The Transformer network, coupled with self-attention mechanisms, efficiently captures high-frequency features during image compression. Howev...
OBJECTIVE: The 3-dimensional (3D) double echo steady state (DESS) magnetic resonance imaging sequence can image knee cartilage with high, isotropic resolution, particularly at high and ultra-high field strengths. Advanced undersampling techniques wit...
In this study, we introduce a novel encoding algorithm utilizing contrastive learning to address the substantial data size challenges inherent in mass spectrometry imaging. Our algorithm compresses MSI data into fixed-length vectors, significantly re...
Neural networks : the official journal of the International Neural Network Society
Apr 25, 2025
Gradient compression with error compensation has attracted significant attention with the target of reducing the heavy communication overhead in distributed learning. However, existing compression methods either perform only unidirectional compressio...
Neural networks : the official journal of the International Neural Network Society
Apr 22, 2025
We propose a novel quaternionic time series compression methodology where we divide a long time series into segments of data, extract the min, max, mean and standard deviation of these chunks as representative features and encapsulate them in a quate...
Electroencephalography (EEG) experiments typically generate vast amounts of data due to the high sampling rates and the use of multiple electrodes to capture brain activity. Consequently, storing and transmitting these large datasets is challenging, ...
Voxelwise encoding models based on convolutional neural networks (CNNs) are widely used as predictive models of brain activity evoked by natural movies. Despite their superior predictive performance, the huge number of parameters in CNN-based models ...
OBJECT: Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements. The aim of this project is to reduce reconstruction...
Neural networks : the official journal of the International Neural Network Society
Jan 22, 2025
Learning from data streams that emerge from nonstationary environments has many real-world applications and poses various challenges. A key characteristic of such a task is the varying nature of the underlying data distributions over time (concept dr...
Neural networks : the official journal of the International Neural Network Society
Jan 21, 2025
This paper proposes a new continual learning method with Bayesian Compression for Shared and Private Latent Representations (BCSPLR), which learns a compact model structure while preserving the accuracy. In Shared and Private Latent Representations (...
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