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Data Compression

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ADA-Tucker: Compressing deep neural networks via adaptive dimension adjustment tucker decomposition.

Neural networks : the official journal of the International Neural Network Society
Despite recent success of deep learning models in numerous applications, their widespread use on mobile devices is seriously impeded by storage and computational requirements. In this paper, we propose a novel network compression method called Adapti...

Deep Generative Adversarial Neural Networks for Compressive Sensing MRI.

IEEE transactions on medical imaging
Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require tradeoffs between accuracy and speed. In addition, state-of-the-art compressed sensing (CS)...

Improving efficiency in convolutional neural networks with multilinear filters.

Neural networks : the official journal of the International Neural Network Society
The excellent performance of deep neural networks has enabled us to solve several automatization problems, opening an era of autonomous devices. However, current deep net architectures are heavy with millions of parameters and require billions of flo...

Learning a variational network for reconstruction of accelerated MRI data.

Magnetic resonance in medicine
PURPOSE: To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning.

ECG data compression using a neural network model based on multi-objective optimization.

PloS one
Electrocardiogram (ECG) data analysis is of great significance to the diagnosis of cardiovascular disease. ECG compression should be processed in real time, and the data should be based on lossless compression and have high predictability. In terms o...

A new near-lossless EEG compression method using ANN-based reconstruction technique.

Computers in biology and medicine
Compression algorithm is an essential part of Telemedicine systems, to store and transmit large amount of medical signals. Most of existing compression methods utilize fixed transforms such as discrete cosine transform (DCT) and wavelet and usually c...

Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation.

PloS one
In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictiona...

Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection.

Computational intelligence and neuroscience
In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-)generative models for acoustic novelty detection ...

Medical Image Retrieval Using Vector Quantization and Fuzzy S-tree.

Journal of medical systems
The aim of the article is to present a novel method for fuzzy medical image retrieval (FMIR) using vector quantization (VQ) with fuzzy signatures in conjunction with fuzzy S-trees. In past times, a task of similar pictures searching was not based on ...

Energy Efficient Monitoring of Metered Dose Inhaler Usage.

Journal of medical systems
Life-long chronic inflammatory diseases of the airways, such as asthma and Chronic Obstructive Pulmonary Disease, are very common worldwide, affecting people of all ages, race and gender. One of the most important aspects for the effective management...