AIMC Topic: Neural Networks, Computer

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Open set recognition algorithm based on Conditional Gaussian Encoder.

Mathematical biosciences and engineering : MBE
For the existing Closed Set Recognition (CSR) methods mistakenly identify unknown jamming signals as a known class, a Conditional Gaussian Encoder (CG-Encoder) for 1-dimensional signal Open Set Recognition (OSR) is designed. The network retains the o...

Learning hidden elasticity with deep neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Elastography is an imaging technique to reconstruct elasticity distributions of heterogeneous objects. Since cancerous tissues are stiffer than healthy ones, for decades, elastography has been applied to medical imaging for noninvasive cancer diagnos...

Closing the gap between single-unit and neural population codes: Insights from deep learning in face recognition.

Journal of vision
Single-unit responses and population codes differ in the "read-out" information they provide about high-level visual representations. Diverging local and global read-outs can be difficult to reconcile with in vivo methods. To bridge this gap, we stud...

Deep learning-based stereophonic acoustic echo suppression without decorrelation.

The Journal of the Acoustical Society of America
Traditional stereophonic acoustic echo cancellation algorithms need to estimate acoustic echo paths from stereo loudspeakers to a microphone, which often suffers from the nonuniqueness problem caused by a high correlation between the two far-end sign...

A multimodel deep learning algorithm to detect North Atlantic right whale up-calls.

The Journal of the Acoustical Society of America
We present a new method of detecting North Atlantic Right Whale (NARW) upcalls using a Multimodel Deep Learning (MMDL) algorithm. A MMDL detector is a classifier that embodies Convolutional Neural Networks (CNNs) and Stacked Auto Encoders (SAEs) and ...

Learn to synchronize, synchronize to learn.

Chaos (Woodbury, N.Y.)
In recent years, the artificial intelligence community has seen a continuous interest in research aimed at investigating dynamical aspects of both training procedures and machine learning models. Of particular interest among recurrent neural networks...

Hidden coexisting firings in fractional-order hyperchaotic memristor-coupled HR neural network with two heterogeneous neurons and its applications.

Chaos (Woodbury, N.Y.)
The firing patterns of each bursting neuron are different because of the heterogeneity, which may be derived from the different parameters or external drives of the same kind of neurons, or even neurons with different functions. In this paper, the di...

Phase-locking intermittency induced by dynamical heterogeneity in networks of thermosensitive neurons.

Chaos (Woodbury, N.Y.)
In this work, we study the phase synchronization of a neural network and explore how the heterogeneity in the neurons' dynamics can lead their phases to intermittently phase-lock and unlock. The neurons are connected through chemical excitatory conne...

Artificial neural network prediction of same-day discharge following primary total knee arthroplasty based on preoperative and intraoperative variables.

The bone & joint journal
AIMS: This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA).