AIMC Topic: Neural Networks, Computer

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A machine-learning approach for long-term prediction of experimental cardiac action potential time series using an autoencoder and echo state networks.

Chaos (Woodbury, N.Y.)
Computational modeling and experimental/clinical prediction of the complex signals during cardiac arrhythmias have the potential to lead to new approaches for prevention and treatment. Machine-learning (ML) and deep-learning approaches can be used fo...

Denoising and feature extraction in photoemission spectra with variational auto-encoder neural networks.

The Review of scientific instruments
In recent years, distinct machine learning (ML) models have been separately used for feature extraction and noise reduction from energy-momentum dispersion intensity maps obtained from raw angle-resolved photoemission spectroscopy (ARPES) data. In th...

Identifying specular highlights: Insights from deep learning.

Journal of vision
Specular highlights are the most important image feature for surface gloss perception. Yet, recognizing whether a bright patch in an image is due to specular reflection or some other cause (e.g., texture marking) is challenging, and it remains unclea...

Could simplified stimuli change how the brain performs visual search tasks? A deep neural network study.

Journal of vision
Visual search is a complex behavior influenced by many factors. To control for these factors, many studies use highly simplified stimuli. However, the statistics of these stimuli are very different from the statistics of the natural images that the h...

Linking task structure and neural network dynamics.

Nature neuroscience
The solutions neural networks find to solve a task are often inscrutable. We have had little insight into why particular structure emerges in a network. By reverse-engineering neural networks from dynamical principles, Dubreuil & Valente et. al. reve...

Weighted sampling-adaptive single-pixel sensing.

Optics letters
The novel single-pixel sensing technique that uses an end-to-end neural network for joint optimization achieves high-level semantic sensing, which is effective but computation-consuming for varied sampling rates. In this Letter, we report a weighted ...

Attention-based neural network for polarimetric image denoising.

Optics letters
In this Letter, we propose an attention-based neural network specially designed for the challenging task of polarimetric image denoising. In particular, the channel attention mechanism is used to effectively extract the features underlying the polari...

Modular Grammatical Evolution for the Generation of Artificial Neural Networks.

Evolutionary computation
This article presents a novel method, called Modular Grammatical Evolution (MGE), toward validating the hypothesis that restricting the solution space of NeuroEvolution to modular and simple neural networks enables the efficient generation of smaller...

Modified generalized neo-fuzzy system with combined online fast learning in medical diagnostic task for situations of information deficit.

Mathematical biosciences and engineering : MBE
In the paper, we propose the modified generalized neo-fuzzy system. It is designed to solve the pattern-image recognition task by working with data that are fed to the system in the image form. The neo-fuzzy system can work with small training datase...