AIMC Topic: Learning

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MLWAN: Multi-Scale Learning Wavelet Attention Module Network for Image Super Resolution.

Sensors (Basel, Switzerland) Nov 24, 2022
Image super resolution (SR) is an important image processing technique in computer vision to improve the resolution of images and videos. In recent years, deep convolutional neural network (CNN) has made significant progress in the field of image SR;...
Neural Networks, Computer Learning Image Processing, Computer-Assisted Records Videotape Recording
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End-to-End Protein Normal Mode Frequency Predictions Using Language and Graph Models and Application to Sonification.

ACS nano Nov 23, 2022
The prediction of mechanical and dynamical properties of proteins is an important frontier, especially given the greater availability of proteins structures. Here we report a series of models that provide end-to-end predictions of nanodynamical prope...
Neural Networks, Computer Learning Proteins Amino Acid Sequence
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SGAT: Shuffle and graph attention based Siamese networks for visual tracking.

PloS one Nov 23, 2022
Siamese-based trackers have achieved excellent performance and attracted extensive attention, which regard the tracking task as a similarity learning between the target template and search regions. However, most Siamese-based trackers do not effectiv...
Neural Networks, Computer Algorithms Learning
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Self-Supervised Action Representation Learning Based on Asymmetric Skeleton Data Augmentation.

Sensors (Basel, Switzerland) Nov 20, 2022
Contrastive learning has received increasing attention in the field of skeleton-based action representations in recent years. Most contrastive learning methods use simple augmentation strategies to construct pairs of positive samples. When using such...
Machine Learning Learning Problem-Based Learning Skeleton
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Forgetting memristor based STDP learning circuit for neural networks.

Neural networks : the official journal of the International Neural Network Society Nov 19, 2022
The circuit implementation of STDP based on memristor is of great significance for the application of neural network. However, recent research shows that the research on the pure circuit implementation of forgetting memristor and STDP is still rare. ...
Neural Networks, Computer Learning Neuronal Plasticity Memory, Short-Term Synapses
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Self-Supervision-Augmented Deep Autoencoder for Unsupervised Visual Anomaly Detection.

IEEE transactions on cybernetics Nov 18, 2022
Deep autoencoder (AE) has demonstrated promising performances in visual anomaly detection (VAD). Learning normal patterns on normal data, deep AE is expected to yield larger reconstruction errors for anomalous samples, which is utilized as the criter...
Learning Deep Learning
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Deep Semisupervised Multiview Learning With Increasing Views.

IEEE transactions on cybernetics Nov 18, 2022
In this article, we study two challenging problems in semisupervised cross-view learning. On the one hand, most existing methods assume that the samples in all views have a pairwise relationship, that is, it is necessary to capture or establish the c...
Artificial Intelligence Algorithms Learning Supervised Machine Learning
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Sparse Bayesian Learning Based on Collaborative Neurodynamic Optimization.

IEEE transactions on cybernetics Nov 18, 2022
Regression in a sparse Bayesian learning (SBL) framework is usually formulated as a global optimization problem with a nonconvex objective function and solved in a majorization-minimization framework where the solution quality and consistency depend ...
Neural Networks, Computer Algorithms Computer Simulation Learning Bayes Theorem
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Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation.

PLoS computational biology Nov 18, 2022
Artificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new training is interleaved with period...
Neural Networks, Computer Brain Learning Sleep
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Reinforcement learning for robust stabilization of nonlinear systems with asymmetric saturating actuators.

Neural networks : the official journal of the International Neural Network Society Nov 16, 2022
We study the robust stabilization problem of a class of nonlinear systems with asymmetric saturating actuators and mismatched disturbances. Initially, we convert such a robust stabilization problem into a nonlinear-constrained optimal control problem...
Neural Networks, Computer Algorithms Learning Feedback Nonlinear Dynamics
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