AIMC Topic: Learning

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A New Method of Image Classification Based on Domain Adaptation.

Sensors (Basel, Switzerland) Feb 9, 2022
Deep neural networks can learn powerful representations from massive amounts of labeled data; however, their performance is unsatisfactory in the case of large samples and small labels. Transfer learning can bridge between a source domain with rich s...
Neural Networks, Computer Learning Acclimatization
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Learning Enhanced Feature Responses for Visual Object Tracking.

Computational intelligence and neuroscience Feb 8, 2022
Visual object tracking is an important topic in computer vision, which has successfully utilized pretrained convolutional neural networks, such as VGG and ResNet. However, the features extracted by these pretrained models are high dimensional, and th...
Neural Networks, Computer Algorithms Learning Attention Psychomotor Performance
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CVDF DYNAMIC-A Dynamic Fuzzy Testing Sample Generation Framework Based on BI-LSTM and Genetic Algorithm.

Sensors (Basel, Switzerland) Feb 7, 2022
As one of the most effective methods of vulnerability mining, fuzzy testing has scalability and complex path detection ability. Fuzzy testing sample generation is the key step of fuzzy testing, and the quality of sample directly determines the vulner...
Neural Networks, Computer Artificial Intelligence Algorithms Forecasting Learning Fuzzy Logic
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Efficient and Stable Graph Scattering Transforms via Pruning.

IEEE transactions on pattern analysis and machine intelligence Feb 3, 2022
Graph convolutional networks (GCNs) have well-documented performance in various graph learning tasks, but their analysis is still at its infancy. Graph scattering transforms (GSTs) offer training-free deep GCN models that extract features from graph ...
Neural Networks, Computer Algorithms Learning
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Meta-Transfer Learning Through Hard Tasks.

IEEE transactions on pattern analysis and machine intelligence Feb 3, 2022
Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few...
Neural Networks, Computer Machine Learning Algorithms Learning
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Orthogonal representations for robust context-dependent task performance in brains and neural networks.

Neuron Jan 31, 2022
How do neural populations code for multiple, potentially conflicting tasks? Here we used computational simulations involving neural networks to define "lazy" and "rich" coding solutions to this context-dependent decision-making problem, which trade o...
Neural Networks, Computer Brain Learning Prefrontal Cortex Task Performance and Analysis
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Cross-modal distribution alignment embedding network for generalized zero-shot learning.

Neural networks : the official journal of the International Neural Network Society Jan 29, 2022
Many approaches in generalized zero-shot learning (GZSL) rely on cross-modal mapping between the image feature space and the class embedding space, which achieves knowledge transfer from seen to unseen classes. However, these two spaces are completel...
Machine Learning Learning Semantics Knowledge Semantic Web
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MedGCN: Medication recommendation and lab test imputation via graph convolutional networks.

Journal of biomedical informatics Jan 29, 2022
Laboratory testing and medication prescription are two of the most important routines in daily clinical practice. Developing an artificial intelligence system that can automatically make lab test imputations and medication recommendations can save co...
Humans Neural Networks, Computer Artificial Intelligence Learning
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GARAT: Generative Adversarial Learning for Robust and Accurate Tracking.

Neural networks : the official journal of the International Neural Network Society Jan 21, 2022
Object tracking by the Siamese network has gained its popularity for its outstanding performance and considerable potential. However, most of the existing Siamese architectures are faced with great difficulties when it comes to the scenes where the t...
Neural Networks, Computer Learning Photic Stimulation
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Enhancing Handover for 5G mmWave Mobile Networks Using Jump Markov Linear System and Deep Reinforcement Learning.

Sensors (Basel, Switzerland) Jan 19, 2022
The Fifth Generation (5G) mobile networks use millimeter waves (mmWaves) to offer gigabit data rates. However, unlike microwaves, mmWave links are prone to user and topographic dynamics. They easily get blocked and end up forming irregular cell patte...
Neural Networks, Computer Artificial Intelligence Learning
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