AIMC Topic: Learning

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Avatar led interventions in the Metaverse reveal that interpersonal effectiveness can be measured, predicted, and improved.

Scientific reports
Experiential learning has been known to be an engaging and effective modality for personal and professional development. The Metaverse provides ample opportunities for the creation of environments in which such experiential learning can occur. In thi...

tension: A Python package for FORCE learning.

PLoS computational biology
First-Order, Reduced and Controlled Error (FORCE) learning and its variants are widely used to train chaotic recurrent neural networks (RNNs), and outperform gradient methods on certain tasks. However, there is currently no standard software framewor...

Distributed Raman Spectrum Data Augmentation System Using Federated Learning with Deep Generative Models.

Sensors (Basel, Switzerland)
Chemical agents are one of the major threats to soldiers in modern warfare, so it is so important to detect chemical agents rapidly and accurately on battlefields. Raman spectroscopy-based detectors are widely used but have many limitations. The Rama...

A Reinforcement Learning-Based Strategy of Path Following for Snake Robots with an Onboard Camera.

Sensors (Basel, Switzerland)
For path following of snake robots, many model-based controllers have demonstrated strong tracking abilities. However, a satisfactory performance often relies on precise modelling and simplified assumptions. In addition, visual perception is also ess...

Sleep-like unsupervised replay reduces catastrophic forgetting in artificial neural networks.

Nature communications
Artificial neural networks are known to suffer from catastrophic forgetting: when learning multiple tasks sequentially, they perform well on the most recent task at the expense of previously learned tasks. In the brain, sleep is known to play an impo...

Reinforcement Learning with Side Information for the Uncertainties.

Sensors (Basel, Switzerland)
Recently, there has been a growing interest in the consensus of a multi-agent system (MAS) with advances in artificial intelligence and distributed computing. Sliding mode control (SMC) is a well-known method that provides robust control in the prese...

Multi-Task Learning Model for Kazakh Query Understanding.

Sensors (Basel, Switzerland)
Query understanding (QU) plays a vital role in natural language processing, particularly in regard to question answering and dialogue systems. QU finds the named entity and query intent in users' questions. Traditional pipeline approaches manage the ...

Sparse RNNs can support high-capacity classification.

PLoS computational biology
Feedforward network models performing classification tasks rely on highly convergent output units that collect the information passed on by preceding layers. Although convergent output-unit like neurons may exist in some biological neural circuits, n...

Flexible Optical Synapses Based on InSe/MoS Heterojunctions for Artificial Vision Systems in the Near-Infrared Range.

ACS applied materials & interfaces
Near-infrared (NIR) synaptic devices integrate NIR optical sensitivity and synaptic plasticity, emulating the basic biomimetic function of the human visual system and showing great potential in NIR artificial vision systems. However, the lack of semi...

Incidental auditory category learning and visuomotor sequence learning do not compete for cognitive resources.

Attention, perception & psychophysics
The environment provides multiple regularities that might be useful in guiding behavior if one was able to learn their structure. Understanding statistical learning across simultaneous regularities is important, but poorly understood. We investigate ...