Results 11 to 20 of about 1,032,480 (246)
Logistic-Normal Likelihoods for Heteroscedastic Label Noise [PDF]
Trans. Mach. Learn. Res., 2023 A natural way of estimating heteroscedastic label noise in regression is to model the observed (potentially noisy) target as a sample from a normal distribution, whose parameters can be learned by minimizing the negative log-likelihood. This formulation has desirable loss attenuation properties, as it reduces the contribution of high-error examples ...
Englesson, Erik +2 more
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Doa Estimation in Heteroscedastic Noise with Sparse Bayesian Learning
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 We consider direction of arrival (DOA) estimation from long-term observations in a noisy environment. In such an environment the noise source might evolve, causing the stationary models to fail. Therefore a heteroscedastic Gaussian noise model is introduced where the variance can vary across observations and sensors.
Peter Gerstoft +3 more
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Effective Causal Discovery under Identifiable Heteroscedastic Noise Model [PDF]
Proceedings of the AAAI Conference on Artificial IntelligenceCapturing the underlying structural causal relations represented by Directed Acyclic Graphs (DAGs) has been a fundamental task in various AI disciplines. Causal DAG learning via the continuous optimization framework has recently achieved promising performance in terms of accuracy and efficiency.
Naiyu Yin +3 more
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Multiobjective Optimization Based on Expensive Robotic Experiments under Heteroscedastic Noise [PDF]
IEEE Transactions on Robotics, 2017 In many engineering problems, including those related to robotics, optimization of the control policy for multiple conflicting criteria is required. However, this can be very challenging because of the existence of noise, which may be input dependent or heteroscedastic, and restrictions regarding the number of evaluations owing to the costliness of the
Ryo Ariizumi +4 more
openaire +4 more sources
IEEE Access, 2021 We consider the problem of uplink signal detection in scalable cell-free mMIMO (CF-mMIMO) systems subject to limited fronthaul link capacity and highly correlated channel conditions.
Kengo Ando +4 more
doaj +1 more source
Twin Least Squares Support Vector Regression of Heteroscedastic Gaussian Noise Model
IEEE Access, 2020 The training algorithm of twin least squares support vector regression (TLSSVR) transforms unequal constraints into equal constraints in a pair of quadratic programming problems, it owns faster computational speed.
Shiguang Zhang +3 more
doaj +1 more source
An improvement on twin parametric-margin support vector machine [PDF]
تصمیم گیری و تحقیق در عملیات, 2022 Purpose: The aim of this paper is to present an enhanced variant of Twin Parametric-Margin Support Vector Machine (TPMSVM) that improves classification performance.Methodology: By replacing a variable in the objective function, we keep the samples of one
Ali Sahleh +2 more
doaj +1 more source
IEEE Access, 2023 Proximal least squares support vector regression is a new regression machine designed by using regularization principle technology and least squares support vector regression.
Ting Zhou, Ge Feng, Shiguang Zhang
doaj +1 more source
Nonparametric Estimation of the Density Function of the Distribution of the Noise in CHARN Models
Mathematics, 2022 This work is concerned with multivariate conditional heteroscedastic autoregressive nonlinear (CHARN) models with an unknown conditional mean function, conditional variance matrix function and density function of the distribution of noise.
Joseph Ngatchou-Wandji +3 more
doaj +1 more source
Streaming Probabilistic PCA for Missing Data with Heteroscedastic Noise [PDF]
, 2023 Streaming principal component analysis (PCA) is an integral tool in large-scale machine learning for rapidly estimating low-dimensional subspaces of very high dimensional and high arrival-rate data with missing entries and corrupting noise.
Fessler, Jeffrey A. +3 more
core +1 more source