AIMC Topic: Generalization, Psychological

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Improving generalization of deep neural networks by leveraging margin distribution.

Neural networks : the official journal of the International Neural Network Society
Recent research has used margin theory to analyze the generalization performance for deep neural networks (DNNs). The existed results are almost based on the spectrally-normalized minimum margin. However, optimizing the minimum margin ignores a mass ...

A Study on Regional GDP Forecasting Analysis Based on Radial Basis Function Neural Network with Genetic Algorithm (RBFNN-GA) for Shandong Economy.

Computational intelligence and neuroscience
Gross domestic product (GDP) is an important indicator for determining a country's or region's economic status and development level, and it is closely linked to inflation, unemployment, and economic growth rates. These basic indicators can comprehen...

NoAS-DS: Neural optimal architecture search for detection of diverse DNA signals.

Neural networks : the official journal of the International Neural Network Society
Neural network architectures are high-performing variable models that can solve many learning tasks. Designing architectures manually require substantial time and also prior knowledge and expertise to develop a high-accuracy model. Most of the archit...

Biological convolutions improve DNN robustness to noise and generalisation.

Neural networks : the official journal of the International Neural Network Society
Deep Convolutional Neural Networks (DNNs) have achieved superhuman accuracy on standard image classification benchmarks. Their success has reignited significant interest in their use as models of the primate visual system, bolstered by claims of thei...

LIO-CSI: LiDAR inertial odometry with loop closure combined with semantic information.

PloS one
Accurate and reliable state estimation and mapping are the foundation of most autonomous driving systems. In recent years, researchers have focused on pose estimation through geometric feature matching. However, most of the works in the literature as...

IC neuron: An efficient unit to construct neural networks.

Neural networks : the official journal of the International Neural Network Society
As a popular machine learning method, neural networks can be used to solve many complex tasks. Their strong generalization ability comes from the representation ability of the basic neuron models. The most popular neuron model is the McCulloch-Pitts ...

TGAN: A simple model update strategy for visual tracking via template-guidance attention network.

Neural networks : the official journal of the International Neural Network Society
Visual attention has been widely used in various fields of visual tasks in recent years. Recently, visual trackers based on probabilistic discriminative model prediction (PrDiMP) and Siamese box adaptive network (SiamBAN) have attracted much attentio...

Neural state space alignment for magnitude generalization in humans and recurrent networks.

Neuron
A prerequisite for intelligent behavior is to understand how stimuli are related and to generalize this knowledge across contexts. Generalization can be challenging when relational patterns are shared across contexts but exist on different physical s...

Why ResNet Works? Residuals Generalize.

IEEE transactions on neural networks and learning systems
Residual connections significantly boost the performance of deep neural networks. However, few theoretical results address the influence of residuals on the hypothesis complexity and the generalization ability of deep neural networks. This article st...

A Hybrid Method Based on Extreme Learning Machine and Self Organizing Map for Pattern Classification.

Computational intelligence and neuroscience
Extreme learning machine is a fast learning algorithm for single hidden layer feedforward neural network. However, an improper number of hidden neurons and random parameters have a great effect on the performance of the extreme learning machine. In o...