Results 41 to 50 of about 1,421,625 (273)
Transfer and Multi-Task Learning for Noun-Noun Compound Interpretation
, 2018 In this paper, we empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task: semantic interpretation of noun--noun compounds.
Fares, Murhaf +2 more
core +1 more source
Online Variational Filtering and Parameter Learning
, 2021 27 pages, 6 figures.
Campbell, A +3 more
openaire +3 more sources
An Adaptive Linear Programming Algorithm with Parameter Learning
AlgorithmsWhen dealing with engineering design problems, designers often encounter nonlinear and nonconvex features, multiple objectives, coupled decision making, and various levels of fidelity of sub-systems.
Lin Guo +4 more
doaj +1 more source
Seismic Signal Classification Using Perceptron With Different Learning Rules
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Perceptron is adopted to classify the Ricker wavelets and to detect the seismic anomaly in a seismogram. Three learning rules are used in the training of perceptron to solve the decision boundary. The optimal learning-rate parameter is derived. The lower
Kou-Yuan Huang +2 more
doaj +1 more source
Learning-parameter adjustment in neural networks [PDF]
Physical Review A, 1992 We present a learning-parameter adjustment algorithm, valid for a large class of learning rules in neural-network literature. The algorithm follows directly from a consideration of the statistics of the weights in the network. The characteristic behavior of the algorithm is calculated, both in a fixed and a changing environment.
Heskes, T., Kappen, H.J.
openaire +4 more sources
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
, 2011 We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. definite clause programs containing probabilistic facts with a parameterized distribution.
Kameya, Y., Sato, T.
core +2 more sources
Digital twins to accelerate target identification and drug development for immune‐mediated disorders
FEBS Open Bio, EarlyView.Digital twins integrate patient‐derived molecular and clinical data into personalised computational models that simulate disease mechanisms. They enable rapid identification and validation of therapeutic targets, prediction of drug responses, and prioritisation of candidate interventions.
Anna Niarakis, Philippe Moingeon
wiley +1 more source
Parameter Tuning Using Gaussian Processes [PDF]
, 2012 Most machine learning algorithms require us to set up their parameter values before applying these algorithms to solve problems. Appropriate parameter settings will bring good performance while inappropriate parameter settings generally result in poor ...
Ma, Jinjin
core +1 more source
Constraining the Parameters of High-Dimensional Models with Active Learning
, 2019 Constraining the parameters of physical models with $>5-10$ parameters is a widespread problem in fields like particle physics and astronomy. The generation of data to explore this parameter space often requires large amounts of computational resources ...
Caron, Sascha +3 more
core +1 more source
Learning Meta-Learning (LML) dataset: Survey data of meta-learning parameters
Data in Brief, 2023 L'ensemble de données « Learning Meta-Learning » présenté dans cet article contient à la fois des données catégorielles et continues d'apprenants adultes pour 7 paramètres de méta-apprentissage : âge, sexe, degré d'illusion de compétence, durée du sommeil, chronotype, expérience du phénomène de l'imposteur et intelligences multiples.
Sonia Corraya +2 more
openaire +3 more sources