Results 101 to 110 of about 27,189 (192)

An Elegant Multi-Agent Gradient Descent for Effective Optimization in Neural Network Training and Beyond

Applied Sciences
Non-convex optimization problems often challenge gradient-based algorithms, such as Gradient Descent. Neural network training, a prominent application of gradient-based methods, heavily relies on their computational efficiency. However, the cost function
Mohammad Sakka, Mohammad Reza Bahrami
doaj   +1 more source

Studying the Thermodynamic Phase Stability of Organic–Inorganic Hybrid Perovskites Using Machine Learning

Molecules
As an important photovoltaic material, organic–inorganic hybrid perovskites have attracted much attention in the field of solar cells, but their instability is one of the main challenges limiting their commercial application.
Juan Wang, Xinzhong Wang, Shun Feng, Zongcheng Miao   +3 more
doaj   +1 more source

Review on Isotonic and Convex Regression

Shape-restricted regression provides a framework for estimating an unknown regression function $f_0: \Omega \subset \mathbb{R}^d \rightarrow \mathbb{R}$ from noisy observations \((\boldsymbol{X}_1, Y_1), \ldots, (\boldsymbol{X}_n, Y_n)\) when no explicit functional relationship between $\boldsymbol{X}$ and $Y$ is known, but $f_0$ is assumed to satisfy ...
openaire   +1 more source

Projection Estimates of Constrained Functional Parameters

AMS classifications: 62G05; 62G07; 62G08; 62G20; 62G32;estimation;convex function;extreme value copula;Pickands dependence function;projection;shape constraint;support function;tangent ...
Segers, J., Guillou, A., Fils-Villetard, A.   +2 more
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A Convex Algorithm for Mixed Linear Regression

, 2017
Mixed linear regression is a high dimensional affine space clustering problem where the goal is to find the parameters of multiple affine spaces that best fit a collection of points.
Joshi, Babhru
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Random gradient-free minimization of convex functions [PDF]

In this paper, we prove the complexity bounds for methods of Convex Optimization based only on computation of the function value. The search directions of our schemes are normally distributed random Gaussian vectors.
NESTEROV, Yurii
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Structural Results on the HMLasso

Axioms
HMLasso (Lasso with High Missing Rate) is a useful technique for sparse regression when a high-dimensional design matrix contains a large number of missing data. To solve HMLasso, an appropriate positive semidefinite symmetric matrix must be obtained. In
Shin-ya Matsushita, Hiromu Sasaki
doaj   +1 more source

Testing for lack of fit in inverse regression - with applications to photonic imaging. [PDF]

Regression; Problems; Lack-of-fit; Applications;
Claeskens, Gerda, Bissantz, N, Holzmann, H, Munk, A   +3 more
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LAD Asymptotics under Conditional Heteroskedasticity with Possibly Infinite Error Densities [PDF]

Least absolute deviations (LAD) estimation of linear time-series models is considered under conditional heteroskedasticity and serial correlation. The limit theory of the LAD estimator is obtained without assuming the finite density condition for the ...
Jin Seo Cho, Peter C.B. Phillips, Chirok Han   +2 more
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Neoclassical versus frontier production models? Testing for the skewness of regression residuals

The empirical literature on production and cost functions is divided into two strands: 1) the neoclassical approach that concentrates on model parameters, 2) the frontier approach that decomposes the disturbance term to a symmetric noise term and a ...
Kuosmanen, Timo, Fosgerau, Mogens
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