AIMC Topic: Learning

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Explainable exercise recommendation with knowledge graph.

Neural networks : the official journal of the International Neural Network Society Dec 5, 2024
Recommending suitable exercises and providing the reasons for these recommendations is a highly valuable task, as it can significantly improve students' learning efficiency. Nevertheless, the extensive range of exercise resources and the diverse lear...
Learning Students Knowledge Deep Learning Humans
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Semantic Mask Reconstruction and Category Semantic Learning for few-shot image generation.

Neural networks : the official journal of the International Neural Network Society Dec 3, 2024
Few-shot image generation aims at generating novel images for the unseen category when given K images from the same category. Despite significant advancements in existing few-shot image generation methods, great challenges remain regarding the qualit...
Humans Learning Image Processing, Computer-Assisted Semantics Neural Networks, Computer Machine Learning
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Improving forward compatibility in class incremental learning by increasing representation rank and feature richness.

Neural networks : the official journal of the International Neural Network Society Dec 2, 2024
Class Incremental Learning (CIL) constitutes a pivotal subfield within continual learning, aimed at enabling models to progressively learn new classification tasks while retaining knowledge obtained from prior tasks. Although previous studies have pr...
Algorithms Learning Neural Networks, Computer Machine Learning Humans
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Teaching design students machine learning to enhance motivation for learning computational thinking skills.

Acta psychologica Dec 2, 2024
The integration of computational thinking (CT) to enhance creativity in design students has often been underexplored in design education. While design thinking has traditionally been the cornerstone of university design pedagogy and remains essential...
Humans Learning Female Machine Learning Motivation Male Young Adult Creativity Teaching Thinking Students Adult Universities
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Generalized zero-shot learning via discriminative and transferable disentangled representations.

Neural networks : the official journal of the International Neural Network Society Nov 30, 2024
In generalized zero-shot learning (GZSL), it is required to identify seen and unseen samples under the condition that only seen classes can be obtained during training. Recent methods utilize disentanglement to make the information contained in visua...
Semantics Humans Machine Learning Neural Networks, Computer Learning
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Riemannian manifold-based disentangled representation learning for multi-site functional connectivity analysis.

Neural networks : the official journal of the International Neural Network Society Nov 29, 2024
Functional connectivity (FC), derived from resting-state functional magnetic resonance imaging (rs-fMRI), has been widely used to characterize brain abnormalities in disorders. FC is usually defined as a correlation matrix that is a symmetric positiv...
Brain Mapping Humans Algorithms Learning Machine Learning Magnetic Resonance Imaging Brain
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Learning performance and physiological feedback-based evaluation for human-robot collaboration.

Applied ergonomics Nov 28, 2024
The development of Industry 4.0 has resulted in tremendous transformations in the manufacturing sector to supplement the human workforce through collaboration with robots. This emphasis on a human-centered approach is a vital aspect in promoting resi...
Learning Female Young Adult Humans Robotics Workload Task Performance and Analysis Adult Male Manufacturing Industry Man-Machine Systems Cooperative Behavior
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Multi-hop interpretable meta learning for few-shot temporal knowledge graph completion.

Neural networks : the official journal of the International Neural Network Society Nov 28, 2024
Multi-hop path completion is a key part of temporal knowledge graph completion, which aims to infer complex relationships and obtain interpretable completion results. However, the traditional multi-hop path completion models mainly focus on the stati...
Knowledge Learning Time Factors Machine Learning Humans Algorithms Neural Networks, Computer
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Revisiting the problem of learning long-term dependencies in recurrent neural networks.

Neural networks : the official journal of the International Neural Network Society Nov 26, 2024
Recurrent neural networks (RNNs) are an important class of models for learning sequential behavior. However, training RNNs to learn long-term dependencies is a tremendously difficult task, and this difficulty is widely attributed to the vanishing and...
Humans Recurrent Neural Networks Neural Networks, Computer Learning
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Spike-VisNet: A novel framework for visual recognition with FocusLayer-STDP learning.

Neural networks : the official journal of the International Neural Network Society Nov 26, 2024
Current vision-inspired spiking neural networks (SNNs) face key challenges due to their model structures typically focusing on single mechanisms and neglecting the integration of multiple biological features. These limitations, coupled with limited s...
Pattern Recognition, Visual Action Potentials Retina Synapses Visual Perception Neurons Neuronal Plasticity Models, Neurological Neural Networks, Computer Learning Humans
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