AI Medical Compendium Topic:
Learning

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Synaptic Scaling-An Artificial Neural Network Regularization Inspired by Nature.

IEEE transactions on neural networks and learning systems
Nature has always inspired the human spirit and scientists frequently developed new methods based on observations from nature. Recent advances in imaging and sensing technology allow fascinating insights into biological neural processes. With the obj...

Contributions by metaplasticity to solving the Catastrophic Forgetting Problem.

Trends in neurosciences
Catastrophic forgetting (CF) refers to the sudden and severe loss of prior information in learning systems when acquiring new information. CF has been an Achilles heel of standard artificial neural networks (ANNs) when learning multiple tasks sequent...

Spatial Iterative Learning Control for Robotic Path Learning.

IEEE transactions on cybernetics
A spatial iterative learning control (sILC) method is proposed for a robot to learn a desired path in an unknown environment. When interacting with the environment, the robot initially starts with a predefined trajectory so an interaction force is ge...

BMT-Net: Broad Multitask Transformer Network for Sentiment Analysis.

IEEE transactions on cybernetics
Sentiment analysis uses a series of automated cognitive methods to determine the author's or speaker's attitudes toward an expressed object or text's overall emotional tendencies. In recent years, the growing scale of opinionated text from social net...

Semisupervised Feature Selection via Structured Manifold Learning.

IEEE transactions on cybernetics
Recently, semisupervised feature selection has gained more attention in many real applications due to the high cost of obtaining labeled data. However, existing methods cannot solve the "multimodality" problem that samples in some classes lie in seve...

A Data-Driven ILC Framework for a Class of Nonlinear Discrete-Time Systems.

IEEE transactions on cybernetics
In this article, we propose a data-driven iterative learning control (ILC) framework for unknown nonlinear nonaffine repetitive discrete-time single-input-single-output systems by applying the dynamic linearization (DL) technique. The ILC law is cons...

Impedance Variation and Learning Strategies in Human-Robot Interaction.

IEEE transactions on cybernetics
In this survey, various concepts and methodologies developed over the past two decades for varying and learning the impedance or admittance of robotic systems that physically interact with humans are explored. For this purpose, the assumptions and ma...

Summarization With Self-Aware Context Selecting Mechanism.

IEEE transactions on cybernetics
In the natural language processing family, learning representations is a pioneering study, especially in sequence-to-sequence tasks where outputs are generated, totally relying on the learning representations of source sequence. Generally, classic me...

Deblurring Dynamic Scenes via Spatially Varying Recurrent Neural Networks.

IEEE transactions on pattern analysis and machine intelligence
Deblurring images captured in dynamic scenes is challenging as the motion blurs are spatially varying caused by camera shakes and object movements. In this paper, we propose a spatially varying neural network to deblur dynamic scenes. The proposed mo...

Student Education Management Strategy Based on Artificial Intelligence Information Model under the Support of 5G Wireless Network.

Computational intelligence and neuroscience
With the popularity of the Internet and the advancement of information technology, more and more people are accepting the teaching and sharing of knowledge through the digitalization of information. The widespread adoption of 5G technology has pushed...