AIMC Topic: Learning

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Multimodal emotional state recognition using sequence-dependent deep hierarchical features.

Neural networks : the official journal of the International Neural Network Society
Emotional state recognition has become an important topic for human-robot interaction in the past years. By determining emotion expressions, robots can identify important variables of human behavior and use these to communicate in a more human-like f...

Development of compositional and contextual communicable congruence in robots by using dynamic neural network models.

Neural networks : the official journal of the International Neural Network Society
The current study presents neurorobotics experiments on acquisition of skills for "communicable congruence" with human via learning. A dynamic neural network model which is characterized by its multiple timescale dynamics property was utilized as a n...

Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model.

PLoS computational biology
Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on rewa...

The foundations of the human cultural niche.

Nature communications
Technological innovations have allowed humans to settle in habitats for which they are poorly suited biologically. However, our understanding of how humans produce complex technologies is limited. We used a computer-based experiment, involving humans...

A study of active learning methods for named entity recognition in clinical text.

Journal of biomedical informatics
OBJECTIVES: Named entity recognition (NER), a sequential labeling task, is one of the fundamental tasks for building clinical natural language processing (NLP) systems. Machine learning (ML) based approaches can achieve good performance, but they oft...

Test-retest reliability of KINARM robot sensorimotor and cognitive assessment: in pediatric ice hockey players.

Journal of neuroengineering and rehabilitation
BACKGROUND: Better diagnostic and prognostic tools are needed to address issues related to early diagnosis and management of concussion across the continuum of aging but particularly in children and adolescents. The purpose of the current study was t...

Networks that learn the precise timing of event sequences.

Journal of computational neuroscience
Neuronal circuits can learn and replay firing patterns evoked by sequences of sensory stimuli. After training, a brief cue can trigger a spatiotemporal pattern of neural activity similar to that evoked by a learned stimulus sequence. Network models s...

Nonlinear Inertia Weighted Teaching-Learning-Based Optimization for Solving Global Optimization Problem.

Computational intelligence and neuroscience
Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continu...

Corticostriatal response selection in sentence production: Insights from neural network simulation with reservoir computing.

Brain and language
Language production requires selection of the appropriate sentence structure to accommodate the communication goal of the speaker - the transmission of a particular meaning. Here we consider event meanings, in terms of predicates and thematic roles, ...

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

IEEE transactions on neural networks and learning systems
Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular mode...