Results 91 to 100 of about 1,249,172 (345)
, 2006 Exhaustive search in relational learning is generally infeasible, therefore some form of heuristic search is usually employed, such as in FOIL[1]. On the other hand, so-called stochastic discrimination provides a framework for combining arbitrary numbers
Anderson, Grant, Pfahringer, Bernhard
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JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
, 2018 In applications of machine learning to particle physics, a persistent challenge is how to go beyond discrimination to learn about the underlying physics.
Andreassen, Anders +3 more
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Advanced Healthcare Materials, EarlyView.AIMSPec‐LoC is a novel lab‐on‐a‐chip platform integrating size‐based extracellular vesicle (EVs) separation with label‐free Raman spectroscopy and AI‐powered classification via SKiNET. This high‐throughput, portable system enables real‐time, multiplexed molecular fingerprinting of EVs from biofluids, offering transformative potential for early, non ...
Emma Buchan +3 more
wiley +1 more source
Strains and stressors: an analysis of touchscreen learning in genetically diverse mouse strains.
PLoS ONE, 2014 Touchscreen-based systems are growing in popularity as a tractable, translational approach for studying learning and cognition in rodents. However, while mouse strains are well known to differ in learning across various settings, performance variation ...
Carolyn Graybeal +7 more
doaj +1 more source
Learning and discrimination through STDP in a top-down modulated associative memory
, 2006 This article underlines the learning and discrimination capabilities of a model of associative memory based on artificial networks of spiking neurons.
Mouraud, Anthony, Paugam-Moisy, Hélène
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Multi-modal discrimination learning in humans: evidence for configural theory
, 2007 Human contingency learning was used to compare the predictions of configural and elemental theories. In three experiments, participants were required to learn which indicators were associated with an increase in core temperature of a fictitious nuclear ...
Redhead, Edward S.
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Discrimination in machine learning algorithms
, 2018 Machine learning algorithms are routinely used for business decisions that may directly affect individuals, for example, because a credit scoring algorithm refuses them a loan. It is then relevant from an ethical (and legal) point of view to ensure that these algorithms do not discriminate based on sensitive attributes (like sex or race), which may ...
Roberta Pappadà, Francesco Pauli
openaire +3 more sources
Advanced Healthcare Materials, EarlyView.This review highlights recent advances in microfluidic technologies for modeling human aging and age‐related diseases. It explores how organ‐on‐chip platforms improve physiological relevance, enable rejuvenation strategies, facilitate drug screening, detect senescent cells, and identify biomarkers.
Limor Zwi‐Dantsis +5 more
wiley +1 more source
On conditional parity as a notion of non-discrimination in machine learning [PDF]
arXiv, 2017 We identify conditional parity as a general notion of non-discrimination in machine learning. In fact, several recently proposed notions of non-discrimination, including a few counterfactual notions, are instances of conditional parity. We show that conditional parity is amenable to statistical analysis by studying randomization as a general mechanism ...
arxiv
Rights and responsibilities: The Disability Discrimination Act (1995) and adults with learning disabilities [PDF]
, 2008 The purpose of this research is to examine Part III (access to goods, facilities and services) of the Disability Discrimination Act (DDA) 1995 in relation to people with learning disabilities.
Lerpiniere, Jennifer, Stalker, Kirsten
core