AIMC Topic: Neural Networks, Computer

Clear Filters Showing 29411 to 29420 of 31376 articles

Effect of Threshold Voltage Window and Variation of Organic Synaptic Transistor for Neuromorphic System.

Journal of nanoscience and nanotechnology
Synaptic devices, which are considered as one of the most important components of neuromorphic system, require a memory effect to store weight values, a high integrity for compact system, and a wide window to guarantee an accurate programming between...

Neural networks may outperform classical regressions, but only when non-linear relationships are considered.

European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery

Pathological Spectra of the Fisher Information Metric and Its Variants in Deep Neural Networks.

Neural computation
The Fisher information matrix (FIM) plays an essential role in statistics and machine learning as a Riemannian metric tensor or a component of the Hessian matrix of loss functions. Focusing on the FIM and its variants in deep neural networks (DNNs), ...

Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting.

Neural computation
Deep learning is often criticized by two serious issues that rarely exist in natural nervous systems: overfitting and catastrophic forgetting. It can even memorize randomly labeled data, which has little knowledge behind the instance-label pairs. Whe...

Storage Capacity of Quaternion-Valued Hopfield Neural Networks With Dual Connections.

Neural computation
A complex-valued Hopfield neural network (CHNN) is a multistate Hopfield model. A quaternion-valued Hopfield neural network (QHNN) with a twin-multistate activation function was proposed to reduce the number of weight parameters of CHNN. Dual connect...

Power Function Error Initialization Can Improve Convergence of Backpropagation Learning in Neural Networks for Classification.

Neural computation
Supervised learning corresponds to minimizing a loss or cost function expressing the differences between model predictions yn and the target values tn given by the training data. In neural networks, this means backpropagating error signals through th...

Randomized Self-Organizing Map.

Neural computation
We propose a variation of the self-organizing map algorithm by considering the random placement of neurons on a two-dimensional manifold, following a blue noise distribution from which various topologies can be derived. These topologies possess rando...

[Application of neural network autoencoder algorithm in the cancer informatics research].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Cancers have been widely recognized as highly heterogeneous diseases, and early diagnosis and prognosis of cancer types have become the focus of cancer research. In the era of big data, efficient mining of massive biomedical data has become a grand c...

Organizing principles of the C. elegans contactome.

Cell systems
Two recent studies published in Nature generate and analyze, for the first time, the network of ∼100,000 membrane contacts between neurons in the C. elegans nerve ring. These novel data, extracted from legacy electron microscographs, represent a shif...