AI Medical Compendium Topic:
Learning

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Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations.

Cognitive science
Decades of psychological research have been aimed at modeling how people learn features and categories. The empirical validation of these theories is often based on artificial stimuli with simple representations. Recently, deep neural networks have r...

Microanalysis of video from a robotic surgical procedure: implications for observational learning in the robotic environment.

Journal of robotic surgery
Without haptic feedback, robotic surgeons rely on visual processing to interpret the operative field. To provide guidance for teaching in this environment, we analyzed intracorporeal actions and behaviors of a robotic surgeon. Six hours of video were...

A Bio-inspired Motivational Decision Making System for Social Robots Based on the Perception of the User.

Sensors (Basel, Switzerland)
Nowadays, many robotic applications require robots making their own decisions and adapting to different conditions and users. This work presents a biologically inspired decision making system, based on drives, motivations, wellbeing, and self-learnin...

Building a knowledge base: Predicting self-derivation through integration in 6- to 10-year-olds.

Journal of experimental child psychology
Self-derivation of new factual knowledge through integration of separate episodes of learning is one means by which children build knowledge. Content generated in this manner becomes incorporated into the knowledge base and is retained over time; suc...

Priors in Animal and Artificial Intelligence: Where Does Learning Begin?

Trends in cognitive sciences
A major goal for the next generation of artificial intelligence (AI) is to build machines that are able to reason and cope with novel tasks, environments, and situations in a manner that approaches the abilities of animals. Evidence from precocial sp...

Fuzzy c-means-based architecture reduction of a probabilistic neural network.

Neural networks : the official journal of the International Neural Network Society
The efficiency of the probabilistic neural network (PNN) is very sensitive to the cardinality of a considered input data set. It results from the design of the network's pattern layer. In this layer, the neurons perform an activation on all input rec...

Machine learning in neurology: what neurologists can learn from machines and vice versa.

Journal of neurology
Artificial intelligence is increasingly becoming a part of everyday life. This raises the question whether clinical neurology can benefit from these novel methods to increase diagnostic accuracy. Several recent studies have used machine learning clas...

An End-to-End System for Automatic Urinary Particle Recognition with Convolutional Neural Network.

Journal of medical systems
The urine sediment analysis of particles in microscopic images can assist physicians in evaluating patients with renal and urinary tract diseases. Manual urine sediment examination is labor-intensive, subjective and time-consuming, and the traditiona...

Learning to activate logic rules for textual reasoning.

Neural networks : the official journal of the International Neural Network Society
Most current textual reasoning models cannotlearn human-like reasoning process, and thus lack interpretability and logical accuracy. To help address this issue, we propose a novel reasoning model which learns to activate logic rules explicitly via de...

Emergent Solutions to High-Dimensional Multitask Reinforcement Learning.

Evolutionary computation
Algorithms that learn through environmental interaction and delayed rewards, or reinforcement learning (RL), increasingly face the challenge of scaling to dynamic, high-dimensional, and partially observable environments. Significant attention is bein...