AIMC Topic: Neural Networks, Computer

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BioDSNN: a dual-stream neural network with hybrid biological knowledge integration for multi-gene perturbation response prediction.

Briefings in bioinformatics
Studying the outcomes of genetic perturbation based on single-cell RNA-seq data is crucial for understanding genetic regulation of cells. However, the high cost of cellular experiments and single-cell sequencing restrict us from measuring the full co...

Measurement of Explanations Generated by XAI Methods Using Features.

Studies in health technology and informatics
An increasing number of explainability methods began to emerge as a response for the black-box methods used to make decisions that could not be easily explained. This created the need for a better evaluation for these methods. In this paper we propos...

Sparse-Coding Variational Autoencoders.

Neural computation
The sparse coding model posits that the visual system has evolved to efficiently code natural stimuli using a sparse set of features from an overcomplete dictionary. The original sparse coding model suffered from two key limitations; however: (1) com...

Determination of neutron spectrum based on artificial neural network using liquid scintillation detector EJ-301.

Radiation protection dosimetry
This paper focuses on the neutron spectrum measurement using a liquid scintillation detector, where the neutron spectrum could be identified and unfolded from the light output distribution of the EJ-301 liquid scintillation detector through a linear ...

AI Prediction for Post-Lower Blepharoplasty Age Reduction.

Aesthetic surgery journal
BACKGROUND: Aesthetic standards vary and are subjective; artificial intelligence (AI), which is currently seeing a boom in interest, has the potential to provide objective assessment.

Sub-sampling graph neural networks for genomic prediction of quantitative phenotypes.

G3 (Bethesda, Md.)
In genomics, use of deep learning (DL) is rapidly growing and DL has successfully demonstrated its ability to uncover complex relationships in large biological and biomedical data sets. With the development of high-throughput sequencing techniques, g...

Convolutional neural networks uncover the dynamics of human visual memory representations over time.

Cerebral cortex (New York, N.Y. : 1991)
The ability to accurately retrieve visual details of past events is a fundamental cognitive function relevant for daily life. While a visual stimulus contains an abundance of information, only some of it is later encoded into long-term memory represe...

Evolving Novel Gene Regulatory Networks for Structural Engineering Designs.

Artificial life
Engineering design optimization poses a significant challenge, usually requiring human expertise to discover superior solutions. Although various search techniques have been employed to generate diverse designs, their effectiveness is often limited b...

Emergence and Criticality in Spatiotemporal Synchronization: The Complementarity Model.

Artificial life
This work concerns the long-term collective excitability properties and the statistical analysis of the critical events displayed by a recently introduced spatiotemporal many-body model, proposed as a new paradigm for Artificial Life. Numerical simul...

Deep convolutional neural networks are sensitive to face configuration.

Journal of vision
Deep convolutional neural networks (DCNNs) are remarkably accurate models of human face recognition. However, less is known about whether these models generate face representations similar to those used by humans. Sensitivity to facial configuration ...