AIMC Topic: Learning

Clear Filters Showing 931 to 940 of 1476 articles

Deep Residual Network in Network.

Computational intelligence and neuroscience
Deep network in network (DNIN) model is an efficient instance and an important extension of the convolutional neural network (CNN) consisting of alternating convolutional layers and pooling layers. In this model, a multilayer perceptron (MLP), a nonl...

Bioinspired multisensory neural network with crossmodal integration and recognition.

Nature communications
The integration and interaction of vision, touch, hearing, smell, and taste in the human multisensory neural network facilitate high-level cognitive functionalities, such as crossmodal integration, recognition, and imagination for accurate evaluation...

The current state of memory Specificity Training (MeST) for emotional disorders.

Current opinion in psychology
Memory Specificity Training (MeST) is an intervention developed from basic science that has found clinical utility. MeST uses cued recall exercises to target the difficulty that some people with emotional disorders have in recalling personally experi...

Reinforcement Learning Approaches in Social Robotics.

Sensors (Basel, Switzerland)
This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Si...

Stochastic configuration network ensembles with selective base models.

Neural networks : the official journal of the International Neural Network Society
Studies have demonstrated that stochastic configuration networks (SCNs) have good potential for rapid data modeling because of their sufficient adequate learning power, which is theoretically guaranteed. Empirical studies have verified that the learn...

A multizone cerebellar chip for bioinspired adaptive robot control and sensorimotor processing.

Journal of the Royal Society, Interface
The cerebellum is a neural structure essential for learning, which is connected via multiple zones to many different regions of the brain, and is thought to improve human performance in a large range of sensory, motor and even cognitive processing ta...

VANTAGE6: an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Answering many of the research questions in the field of cancer informatics requires incorporating and centralizing data that are hosted by different parties. Federated Learning (FL) has emerged as a new approach in which a global model can be genera...

Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The potential of Reinforcement Learning (RL) has been demonstrated through successful applications to games such as Go and Atari. However, while it is straightforward to evaluate the performance of an RL algorithm in a game setting by simply using it...

PsychRNN: An Accessible and Flexible Python Package for Training Recurrent Neural Network Models on Cognitive Tasks.

eNeuro
Task-trained artificial recurrent neural networks (RNNs) provide a computational modeling framework of increasing interest and application in computational, systems, and cognitive neuroscience. RNNs can be trained, using deep-learning methods, to per...