AIMC Topic: Learning

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Local augmentation based consistency learning for semi-supervised pathology image classification.

Computer methods and programs in biomedicine Feb 24, 2023
BACKGROUND AND OBJECTIVE: Labeling pathology images is often costly and time-consuming, which is quite detrimental for supervised pathology image classification that relies heavily on sufficient labeled data during training. Exploring semi-supervised...
Learning Supervised Machine Learning
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Decoupled neural network training with re-computation and weight prediction.

PloS one Feb 23, 2023
To break the three lockings during backpropagation (BP) process for neural network training, multiple decoupled learning methods have been investigated recently. These methods either lead to significant drop in accuracy performance or suffer from dra...
Humans Neural Networks, Computer Learning Memory Disorders
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Lifelong Text-Audio Sentiment Analysis learning.

Neural networks : the official journal of the International Neural Network Society Feb 17, 2023
Sentiment analysis refers to the mining of textual context, which is conducted with the aim of identifying and extracting subjective opinions in textual materials. However, most existing methods neglect other important modalities, e.g., the audio mod...
Machine Learning Learning Semantics Knowledge Sentiment Analysis
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Commonsense psychology in human infants and machines.

Cognition Feb 16, 2023
Human infants are fascinated by other people. They bring to this fascination a constellation of rich and flexible expectations about the intentions motivating people's actions. Here we test 11-month-old infants and state-of-the-art learning-driven ne...
Humans Artificial Intelligence Learning Infant Intention Psychology, Child
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UDRN: Unified Dimensional Reduction Neural Network for feature selection and feature projection.

Neural networks : the official journal of the International Neural Network Society Feb 15, 2023
Dimensional reduction (DR) maps high-dimensional data into a lower dimensions latent space with minimized defined optimization objectives. The two independent branches of DR are feature selection (FS) and feature projection (FP). FS focuses on select...
Neural Networks, Computer Learning Generalization, Psychological
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HCTNet: A hybrid CNN-transformer network for breast ultrasound image segmentation.

Computers in biology and medicine Feb 9, 2023
Automatic breast ultrasound image segmentation helps radiologists to improve the accuracy of breast cancer diagnosis. In recent years, the convolutional neural networks (CNNs) have achieved great success in medical image analysis. However, it exhibit...
Humans Neural Networks, Computer Learning Image Processing, Computer-Assisted Radiologists Semantics
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Resolving the associative learning paradox by category learning in pigeons.

Current biology : CB Feb 7, 2023
A wealth of evidence indicates that humans can engage two types of mechanisms to solve category-learning tasks: declarative mechanisms, which involve forming and testing verbalizable decision rules, and associative mechanisms, which involve gradually...
Artificial Intelligence Learning Animals Concept Formation Columbidae Conditioning, Classical
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The role of capacity constraints in Convolutional Neural Networks for learning random versus natural data.

Neural networks : the official journal of the International Neural Network Society Feb 4, 2023
Convolutional neural networks (CNNs) are often described as promising models of human vision, yet they show many differences from human abilities. We focus on a superhuman capacity of top-performing CNNs, namely, their ability to learn very large dat...
Humans Neural Networks, Computer Learning Generalization, Psychological
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Continual Object Detection: A review of definitions, strategies, and challenges.

Neural networks : the official journal of the International Neural Network Society Feb 4, 2023
The field of Continual Learning investigates the ability to learn consecutive tasks without losing performance on those previously learned. The efforts of researchers have been mainly focused on incremental classification tasks. Yet, we believe that ...
Learning Robotics
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Learning matrix factorization with scalable distance metric and regularizer.

Neural networks : the official journal of the International Neural Network Society Feb 3, 2023
Matrix factorization has always been an encouraging field, which attempts to extract discriminative features from high-dimensional data. However, it suffers from negative generalization ability and high computational complexity when handling large-sc...
Neural Networks, Computer Algorithms Forecasting Learning Generalization, Psychological
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