AIMC Topic: Learning

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Artificial intelligence to support human instruction.

Proceedings of the National Academy of Sciences of the United States of America

How the input shapes the acquisition of verb morphology: Elicited production and computational modelling in two highly inflected languages.

Cognitive psychology
The aim of the present work was to develop a computational model of how children acquire inflectional morphology for marking person and number; one of the central challenges in language development. First, in order to establish which putative learnin...

Gated Orthogonal Recurrent Units: On Learning to Forget.

Neural computation
We present a novel recurrent neural network (RNN)-based model that combines the remembering ability of unitary evolution RNNs with the ability of gated RNNs to effectively forget redundant or irrelevant information in its memory. We achieve this by e...

A stochastic variational framework for Recurrent Gaussian Processes models.

Neural networks : the official journal of the International Neural Network Society
Gaussian Processes (GPs) models have been successfully applied to the problem of learning from sequential observations. In such context, the family of Recurrent Gaussian Processes (RGPs) have been recently introduced with a specifically designed stru...

A model of event knowledge.

Psychological review
Our knowledge of events and situations in the world plays a critical role in our ability to understand what is happening around us, to predict what might happen next, and to comprehend language. What has not been so clear is the form and structure of...

Who is a better teacher for children with autism? Comparison of learning outcomes between robot-based and human-based interventions in gestural production and recognition.

Research in developmental disabilities
BACKGROUND: Individuals with autism spectrum disorder (ASD) tend to show deficits in engaging with humans. Previous findings have shown that robot-based training improves the gestural recognition and production of children with ASD. It is not known w...

Task representations in neural networks trained to perform many cognitive tasks.

Nature neuroscience
The brain has the ability to flexibly perform many tasks, but the underlying mechanism cannot be elucidated in traditional experimental and modeling studies designed for one task at a time. Here, we trained single network models to perform 20 cogniti...

Multi-task learning improves ancestral state reconstruction.

Theoretical population biology
We consider the ancestral state reconstruction problem where we need to infer phenotypes of ancestors using observations from present-day species. For this problem, we propose a multi-task learning method that uses regularized maximum likelihood to e...

Sensorimotor Robotic Measures of tDCS- and HD-tDCS-Enhanced Motor Learning in Children.

Neural plasticity
Transcranial direct-current stimulation (tDCS) enhances motor learning in adults. We have demonstrated that anodal tDCS and high-definition (HD) tDCS of the motor cortex can enhance motor skill acquisition in children, but behavioral mechanisms remai...

A Neural Machine Translation Model for Arabic Dialects That Utilises Multitask Learning (MTL).

Computational intelligence and neuroscience
In this research article, we study the problem of employing a neural machine translation model to translate Arabic dialects to modern standard Arabic. The proposed solution of the neural machine translation model is prompted by the recurrent neural n...