AIMC Topic: Learning

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Noise Correlations for Faster and More Robust Learning.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Distributed population codes are ubiquitous in the brain and pose a challenge to downstream neurons that must learn an appropriate readout. Here we explore the possibility that this learning problem is simplified through inductive biases implemented ...

Using an Optimized Learning Vector Quantization- (LVQ-) Based Neural Network in Accounting Fraud Recognition.

Computational intelligence and neuroscience
With the continuous development and wide application of artificial intelligence technology, artificial neural network technology has begun to be used in the field of fraud identification. Among them, learning vector quantization (LVQ) neural network ...

Learning exact enumeration and approximate estimation in deep neural network models.

Cognition
A system for approximate number discrimination has been shown to arise in at least two types of hierarchical neural network models-a generative Deep Belief Network (DBN) and a Hierarchical Convolutional Neural Network (HCNN) trained to classify natur...

On the effective initialisation for restricted Boltzmann machines via duality with Hopfield model.

Neural networks : the official journal of the International Neural Network Society
Restricted Boltzmann machines (RBMs) with a binary visible layer of size N and a Gaussian hidden layer of size P have been proved to be equivalent to a Hopfield neural network (HNN) made of N binary neurons and storing P patterns ξ, as long as the we...

Sensitivity - Local index to control chaoticity or gradient globally.

Neural networks : the official journal of the International Neural Network Society
Here, we introduce a fully local index named "sensitivity" for each neuron to control chaoticity or gradient globally in a neural network (NN). We also propose a learning method to adjust it named "sensitivity adjustment learning (SAL)". The index is...

Learning and confirming a class of treatment responders in clinical trials.

Statistics in medicine
Clinical trials require substantial effort and time to complete, and regulatory agencies may require two successful efficacy trials before approving a new drug. One way to improve the chance of follow-up success is to identify a subpopulation among w...

Multi-Nyström Method Based on Multiple Kernel Learning for Large Scale Imbalanced Classification.

Computational intelligence and neuroscience
Extensions of kernel methods for the class imbalance problems have been extensively studied. Although they work well in coping with nonlinear problems, the high computation and memory costs severely limit their application to real-world imbalanced ta...

Scaling Human-Object Interaction Recognition in the Video through Zero-Shot Learning.

Computational intelligence and neuroscience
Recognition of human activities is an essential field in computer vision. The most human activity consists of the interaction between humans and objects. Many successful works have been done on human-object interaction (HOI) recognition and achieved ...

Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals.

Scientific reports
This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning a...

A hybrid quantum-classical neural network with deep residual learning.

Neural networks : the official journal of the International Neural Network Society
Inspired by the success of classical neural networks, there has been tremendous effort to develop classical effective neural networks into quantum concept. In this paper, a novel hybrid quantum-classical neural network with deep residual learning (Re...