AIMC Topic: Neural Networks, Computer

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Sleep-like unsupervised replay reduces catastrophic forgetting in artificial neural networks.

Nature communications
Artificial neural networks are known to suffer from catastrophic forgetting: when learning multiple tasks sequentially, they perform well on the most recent task at the expense of previously learned tasks. In the brain, sleep is known to play an impo...

Accuracy and data efficiency in deep learning models of protein expression.

Nature communications
Synthetic biology often involves engineering microbial strains to express high-value proteins. Thanks to progress in rapid DNA synthesis and sequencing, deep learning has emerged as a promising approach to build sequence-to-expression models for stra...

Tongue Image Texture Classification Based on Image Inpainting and Convolutional Neural Network.

Computational and mathematical methods in medicine
Tongue texture analysis is of importance to inspection diagnosis in traditional Chinese medicine (TCM), which has great application and irreplaceable value. The tough and tender classification for tongue image relies mainly on image texture of tongue...

Using cascade CNN-LSTM-FCNs to identify AI-altered video based on eye state sequence.

PloS one
Deep learning is notably successful in data analysis, computer vision, and human control. Nevertheless, this approach has inevitably allowed the development of DeepFake video sequences and images that could be altered so that the changes are not easi...

U-SPDNet: An SPD manifold learning-based neural network for visual classification.

Neural networks : the official journal of the International Neural Network Society
With the development of neural networking techniques, several architectures for symmetric positive definite (SPD) matrix learning have recently been put forward in the computer vision and pattern recognition (CV&PR) community for mining fine-grained ...

Observer-based dynamical pattern recognition via deterministic learning.

Neural networks : the official journal of the International Neural Network Society
In this paper, based on the sampled-data observer and the deterministic learning theory, a rapid dynamical pattern recognition approach is proposed for univariate time series composed of the output signals of the dynamical systems. Specifically, loca...

Early stopping by correlating online indicators in neural networks.

Neural networks : the official journal of the International Neural Network Society
In order to minimize the generalization error in neural networks, a novel technique to identify overfitting phenomena when training the learner is formally introduced. This enables support of a reliable and trustworthy early stopping condition, thus ...

Research on cell detection method for microfluidic single cell dispensing.

Mathematical biosciences and engineering : MBE
Single cell dispensing techniques mainly include limiting dilution, fluorescent-activated cell sorting (FACS) and microfluidic approaches. Limiting dilution process is complicated by statistical analysis of clonally derived cell lines. Flow cytometry...

Deep Learning for Image Analysis in Kidney Care.

Advances in kidney disease and health
Analysis of medical images, such as radiological or tissue specimens, is an indispensable part of medical diagnostics. Conventionally done manually, the process may sometimes be time-consuming and prone to interobserver variability. Image classificat...

Transformer-based multilevel region and edge aggregation network for magnetic resonance image segmentation.

Computers in biology and medicine
To improve the quality of magnetic resonance (MR) image edge segmentation, some researchers applied additional edge labels to train the network to extract edge information and aggregate it with region information. They have made significant progress....