AI Medical Compendium Topic:
Learning

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Probabilistic generative modeling and reinforcement learning extract the intrinsic features of animal behavior.

Neural networks : the official journal of the International Neural Network Society
It is one of the ultimate goals of ethology to understand the generative process of animal behavior, and the ability to reproduce and control behavior is an important step in this field. However, it is not easy to achieve this goal in systems with co...

Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training.

PloS one
Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate th...

A Multitask Learning Model with Multiperspective Attention and Its Application in Recommendation.

Computational intelligence and neuroscience
Training models to predict click and order targets at the same time. For better user satisfaction and business effectiveness, multitask learning is one of the most important methods in e-commerce. Some existing researches model user representation ba...

A failure to learn object shape geometry: Implications for convolutional neural networks as plausible models of biological vision.

Vision research
Here we examine the plausibility of deep convolutional neural networks (CNNs) as a theoretical framework for understanding biological vision in the context of image classification. Recent work on object recognition in human vision has shown that both...

Investigation of Heterogeneity Sources for Occupational Task Recognition via Transfer Learning.

Sensors (Basel, Switzerland)
Human activity recognition has been extensively used for the classification of occupational tasks. Existing activity recognition approaches perform well when training and testing data follow an identical distribution. However, in the real world, this...

An Incremental Class-Learning Approach with Acoustic Novelty Detection for Acoustic Event Recognition.

Sensors (Basel, Switzerland)
Acoustic scene analysis (ASA) relies on the dynamic sensing and understanding of stationary and non-stationary sounds from various events, background noises and human actions with objects. However, the spatio-temporal nature of the sound signals may ...

Extreme neural machines.

Neural networks : the official journal of the International Neural Network Society
Recurrent neural networks can solve a variety of computational tasks and produce patterns of activity that capture key properties of brain circuits. However, learning rules designed to train these models are time-consuming and prone to inaccuracies w...

Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging.

Nature communications
Medical imaging is a central part of clinical diagnosis and treatment guidance. Machine learning has increasingly gained relevance because it captures features of disease and treatment response that are relevant for therapeutic decision-making. In cl...

Disturbance-immune weight sharing for neural architecture search.

Neural networks : the official journal of the International Neural Network Society
Neural architecture search (NAS) has gained increasing attention in the community of architecture design. One of the key factors behind the success lies in the training efficiency brought by the weight sharing (WS) technique. However, WS-based NAS me...

Parallel and hierarchical neural mechanisms for adaptive and predictive behavioral control.

Neural networks : the official journal of the International Neural Network Society
Our brain can be recognized as a network of largely hierarchically organized neural circuits that operate to control specific functions, but when acting in parallel, enable the performance of complex and simultaneous behaviors. Indeed, many of our da...