AIMC Topic: Data Compression

Clear Filters Showing 1 to 10 of 150 articles

Efficient Compression of Mass Spectrometry Images via Contrastive Learning-Based Encoding.

Analytical chemistry Jul 21, 2025
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...
Humans Machine Learning Barrett Esophagus Urinary Bladder Data Compression Image Processing, Computer-Assisted Mice Mass Spectrometry Algorithms Animals
View on PubMed DOI

S2LIC: Learned image compression with the SwinV2 block, Adaptive Channel-wise and Global-inter attention Context.

Neural networks : the official journal of the International Neural Network Society May 19, 2025
Recently, deep learning technology has been successfully applied in the field of image compression, leading to superior rate-distortion performance. It is crucial to design an effective and efficient entropy model to estimate the probability distribu...
Algorithms Deep Learning Image Processing, Computer-Assisted Humans Neural Networks, Computer Entropy Attention Data Compression
View on PubMed DOI

Communication-efficient distributed learning with Local Immediate Error Compensation.

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...
Communication Deep Learning Data Compression Machine Learning Algorithms Humans Neural Networks, Computer
View on PubMed DOI

Time series compression using quaternion valued neural networks and quaternion backpropagation.

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...
Algorithms Humans Time Factors Neural Networks, Computer Data Compression
View on PubMed DOI

Near-lossless EEG signal compression using a convolutional autoencoder: Case study for 256-channel binocular rivalry dataset.

Computers in biology and medicine Mar 5, 2025
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, ...
Neural Networks, Computer Electroencephalography Data Compression Autoencoder Signal Processing, Computer-Assisted Humans
View on PubMed DOI

Compression-enabled interpretability of voxelwise encoding models.

PLoS computational biology Feb 19, 2025
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 ...
Neural Networks, Computer Algorithms Computational Biology Models, Neurological Brain Humans Principal Component Analysis Data Compression
View on PubMed DOI

Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction.

Magma (New York, N.Y.) Feb 1, 2025
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...
Data Compression Brain Mapping Phantoms, Imaging Spatio-Temporal Analysis Humans Deep Learning Algorithms Image Processing, Computer-Assisted Imaging, Three-Dimensional Brain Magnetic Resonance Imaging
View on PubMed DOI

Online ensemble model compression for nonstationary data stream learning.

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...
Learning Humans Neural Networks, Computer Data Compression Online Systems Machine Learning Algorithms
View on PubMed DOI

Continual learning with Bayesian compression for shared and private latent representations.

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 (...
Learning Humans Bayes Theorem Machine Learning Neural Networks, Computer Data Compression Algorithms
View on PubMed DOI

An end-to-end implicit neural representation architecture for medical volume data.

PloS one Jan 3, 2025
Medical volume data are rapidly increasing, growing from gigabytes to petabytes, which presents significant challenges in organisation, storage, transmission, manipulation, and rendering. To address the challenges, we propose an end-to-end architectu...
Data Compression Magnetic Resonance Imaging Signal-To-Noise Ratio Algorithms Deep Learning Humans Neural Networks, Computer Image Processing, Computer-Assisted
View on PubMed DOI
Popular Topics
Recent Journals