Concept

Non-linear least squares

Related publications (238)

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections

We study the least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kernel Hilbert space as a special case. We rst investigate regularized algorithms adapted to a projection operator on a closed subspace ...

2020

Learning (from) the Electron Density: Transferability, Conformational and Chemical Diversity

Ksenia Briling, Alberto Fabrizio, Andrea Grisafi

Machine-learning in quantum chemistry is currently booming, with reported applications spanning all molecular properties from simple atomization energies to complex mathematical objects such as the many-body wavefunction. Due to its central role in density ...

2020

Machine learning models of the energy curvature vs particle number for optimal tuning of long-range corrected functionals

Alberto Fabrizio, Benjamin André René Meyer

The average energy curvature as a function of the particle number is a molecule-specific quantity, which measures the deviation of a given functional from the exact conditions of density functional theory. Related to the lack of derivative discontinuity in ...

AMER INST PHYSICS2020

Coordinate Difference Matrices

Martin Vetterli, Gilles Baechler, Miranda Krekovic, Golnooshsadat Elhami

In many problems such as phase retrieval, molecular biology, source localization, and sensor array calibration, one can measure vector differences between pairs of points and attempt to recover the position of these points; this class of problems is called ...

SIAM PUBLICATIONS2020

Age-Dependent Asymmetry of Wrist Position Sense Is Not Influenced by Stochastic Tactile Stimulation

Jamie Paik, Olivier Lambercy, Harshal Arun Sonar

Stochastic stimulation has been shown to improve movement, balance, the sense of touch, and may also improve position sense. This stimulation can be non-invasive and may be a simple technology to enhance proprioception. In this study, we investigated wheth ...

2020

MATHICSE Technical Report: Existence of dynamical low rank approximations for random semi-linear evolutionary equations on the maximal interval

Fabio Nobile, Yoshihito Kazashi

An existence result is presented for the dynamical low rank (DLR) approximation for random semi-linear evolutionary equations. The DLR solution approximates the true solution at each time instant by a linear combination of products of deterministic and sto ...

MATHICSE2020

Existence of dynamical low rank approximations for random semi-linear evolutionary equations on the maximal interval

Fabio Nobile, Yoshihito Kazashi

2020

On the choice of metric in gradient-based theories of brain function

Wulfram Gerstner, Johanni Michael Brea, Jean-Pascal Théodor Pfister

The idea that the brain functions so as to minimize certain costs pervades theoretical neuroscience. Because a cost function by itself does not predict how the brain finds its minima, additional assumptions about the optimization method need to be made to ...

2020

Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval

Florent Gérard Krzakala, Lenka Zdeborová

Despite the widespread use of gradient-based algorithms for optimizing high-dimensional non-convex functions, understanding their ability of finding good minima instead of being trapped in spurious ones remains to a large extent an open problem. Here we fo ...

Curran Associates, Inc.2020

On raw inertial measurements in dynamic networks

Jan Skaloud, Davide Antonio Cucci

Dynamic Networks have been introduced in the literature to solve multi-sensor fusion problems for navigation and mapping. They have been shown to outperform conventional methods in challenging scenarios, such as corridor mapping or self-calibration. In thi ...

2019

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