AI Medical Compendium Topic:
Learning

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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...

Deep Differentiable Random Forests for Age Estimation.

IEEE transactions on pattern analysis and machine intelligence
Age estimation from facial images is typically cast as a label distribution learning or regression problem, since aging is a gradual progress. Its main challenge is the facial feature space w.r.t. ages is inhomogeneous, due to the large variation in ...

Human locomotion with reinforcement learning using bioinspired reward reshaping strategies.

Medical & biological engineering & computing
Recent learning strategies such as reinforcement learning (RL) have favored the transition from applied artificial intelligence to general artificial intelligence. One of the current challenges of RL in healthcare relates to the development of a cont...

Text Semantic Classification of Long Discourses Based on Neural Networks with Improved Focal Loss.

Computational intelligence and neuroscience
Semantic classification of Chinese long discourses is an important and challenging task. Discourse text is high-dimensional and sparse. Furthermore, when the number of classes of dataset is large, the data distribution will be seriously imbalanced. I...

A deep-learning-based unsupervised model on esophageal manometry using variational autoencoder.

Artificial intelligence in medicine
High-resolution manometry (HRM) is the primary method for diagnosing esophageal motility disorders and its interpretation and classification are based on variables (features) from data of each swallow. Modeling and learning the semantics directly fro...

Multifrequency Hebbian plasticity in coupled neural oscillators.

Biological cybernetics
We study multifrequency Hebbian plasticity by analyzing phenomenological models of weakly connected neural networks. We start with an analysis of a model for single-frequency networks previously shown to learn and memorize phase differences between c...

Systematic Review on Which Analytics and Learning Methodologies Are Applied in Primary and Secondary Education in the Learning of Robotics Sensors.

Sensors (Basel, Switzerland)
Robotics technology has become increasingly common both for businesses and for private citizens. Primary and secondary schools, as a mirror of societal evolution, have increasingly integrated science, technology, engineering and math concepts into th...