AI Medical Compendium Topic:
Learning

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Estimating individualized treatment regimes from crossover designs.

Biometrics
The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way. To this end, estimating an optimal individualized treatment regime (ITR) that recommends treatment decisions based on patient characteri...

Structured sequence processing and combinatorial binding: neurobiologically and computationally informed hypotheses.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Understanding how the brain forms representations of structured information distributed in time is a challenging endeavour for the neuroscientific community, requiring computationally and neurobiologically informed approaches. The neural mechanisms f...

Training neural networks to encode symbols enables combinatorial generalization.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Combinatorial generalization-the ability to understand and produce novel combinations of already familiar elements-is considered to be a core capacity of the human mind and a major challenge to neural network models. A significant body of research su...

Linguistic generalization and compositionality in modern artificial neural networks.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
In the last decade, deep artificial neural networks have achieved astounding performance in many natural language-processing tasks. Given the high productivity of language, these models must possess effective generalization abilities. It is widely as...

Reaction-diffusion memory unit: Modeling of sensitization, habituation and dishabituation in the brain.

PloS one
We propose a novel approach to investigate the effects of sensitization, habituation and dishabituation in the brain using the analysis of the reaction-diffusion memory unit (RDMU). This unit consists of Morris-Lecar-type sensory, motor, interneuron ...

Is coding a relevant metaphor for building AI?

The Behavioral and brain sciences
Brette contends that the neural coding metaphor is an invalid basis for theories of what the brain does. Here, we argue that it is an insufficient guide for building an artificial intelligence that learns to accomplish short- and long-term goals in a...

Ease of learning explains semantic universals.

Cognition
Semantic universals are properties of meaning shared by the languages of the world. We offer an explanation of the presence of such universals by measuring simplicity in terms of ease of learning, showing that expressions satisfying universals are si...

Reinforcement Learning for Improving Agent Design.

Artificial life
In many reinforcement learning tasks, the goal is to learn a policy to manipulate an agent, whose design is fixed, to maximize some notion of cumulative reward. The design of the agent's physical structure is rarely optimized for the task at hand. In...

A neural model of schemas and memory encoding.

Biological cybernetics
The ability to rapidly assimilate new information is essential for survival in a dynamic environment. This requires experiences to be encoded alongside the contextual schemas in which they occur. Tse et al. (Science 316(5821):76-82, 2007) showed that...

Humans and machines: Moving towards a more symbiotic approach to learning clinical reasoning.

Medical teacher
Artificial intelligence is a growing phenomenon that is driving major changes to how we deliver healthcare. One of its most significant and challenging contributions is likely to be in diagnosis. Artificial intelligence is challenging the physician's...