AIMC Topic: Learning

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VISAL-A novel learning strategy to address class imbalance.

Neural networks : the official journal of the International Neural Network Society Jan 20, 2023
In the imbalance data scenarios, Deep Neural Networks (DNNs) fail to generalize well on minority classes. In this letter, we propose a simple and effective learning function i.e, Visually Interpretable Space Adjustment Learning (VISAL) to handle the ...
Generalization, Psychological Algorithms Machine Learning Learning Neural Networks, Computer
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A Study on the Role of Affective Feedback in Robot-Assisted Learning.

Sensors (Basel, Switzerland) Jan 20, 2023
In recent years, there have been many approaches to using robots to teach computer programming. In intelligent tutoring systems and computer-aided learning, there is also some research to show that affective feedback to the student increases learning...
Emotions Robotics Pilot Projects Humans Learning Feedback
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Prediction and Big Data Impact Analysis of Telecom Churn by Backpropagation Neural Network Algorithm from the Perspective of Business Model.

Big data Jan 19, 2023
This study aims to transform the existing telecom operators from traditional Internet operators to digital-driven services, and improve the overall competitiveness of telecom enterprises. Data mining is applied to telecom user classification to proce...
Data Mining Algorithms Big Data Learning Neural Networks, Computer
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Intra-person multi-task learning method for chronic-disease prediction.

Scientific reports Jan 19, 2023
In the medical field, various clinical information has been accumulated to help clinicians provide personalized medicine and make better diagnoses. As chronic diseases share similar characteristics, it is possible to predict multiple chronic diseases...
Chronic Disease Neural Networks, Computer Time Factors Learning Humans
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Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals.

PLoS computational biology Jan 19, 2023
Humans can learn several tasks in succession with minimal mutual interference but perform more poorly when trained on multiple tasks at once. The opposite is true for standard deep neural networks. Here, we propose novel computational constraints for...
Humans Learning Animals Machine Learning Neural Networks, Computer Curriculum Prefrontal Cortex
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Recurrent networks endowed with structural priors explain suboptimal animal behavior.

Current biology : CB Jan 18, 2023
The strategies found by animals facing a new task are determined both by individual experience and by structural priors evolved to leverage the statistics of natural environments. Rats quickly learn to capitalize on the trial sequence correlations of...
Choice Behavior Learning Neural Networks, Computer Rats Behavior, Animal Animals
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Self-attention learning network for face super-resolution.

Neural networks : the official journal of the International Neural Network Society Jan 14, 2023
Existing face super-resolution methods depend on deep convolutional networks (DCN) to recover high-quality reconstructed images. They either acquire information in a single space by designing complex models for direct reconstruction, or employ additi...
Attention Image Processing, Computer-Assisted Deep Learning Learning
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Autonomous Driving Control Based on the Technique of Semantic Segmentation.

Sensors (Basel, Switzerland) Jan 12, 2023
Advanced Driver Assistance Systems (ADAS) are only applied to relatively simple scenarios, such as highways. If there is an emergency while driving, the driver should take control of the car to deal properly with the situation at any time. Obviously,...
Automobile Driving Autonomous Vehicles Semantics Artificial Intelligence Learning
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MM-StackEns: A new deep multimodal stacked generalization approach for protein-protein interaction prediction.

Computers in biology and medicine Jan 3, 2023
Accurate in-silico identification of protein-protein interactions (PPIs) is a long-standing problem in biology, with important implications in protein function prediction and drug design. Current computational approaches predominantly use a single da...
Humans Neural Networks, Computer Amino Acid Sequence Saccharomyces cerevisiae Proteins Learning
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Efficient neural codes naturally emerge through gradient descent learning.

Nature communications Dec 29, 2022
Human sensory systems are more sensitive to common features in the environment than uncommon features. For example, small deviations from the more frequently encountered horizontal orientations can be more easily detected than small deviations from t...
Neural Networks, Computer Learning Humans
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