Results 91 to 100 of about 13,759,341 (300)

Stabilization Learning: A Paradigm Transition Bridging Control Theory and Machine Learning

Stabilization learning is an interdisciplinary paradigm that bridges control theory and machine learning. Its core idea is to enable systems to adjust their policies under perturbations or environmental changes through real-time feedback and adaptive mechanisms.
openaire   +2 more sources

Stability of randomized learning algorithms

, 2005
We extend existing theory on stability, namely how much changes in the training data influence the estimated models, and generalization performance of deterministic learning algorithms to the case of randomized algorithms.
Pack Kaelbling, Andre Elisseeff, Theodoros Evgeniou, Massimiliano Pontil   +3 more
core  

Accurate screening of functional materials with machine-learning potential and transfer-learned regressions: Heusler alloy benchmark

npj Computational Materials
We present a machine learning-accelerated high-throughput (HTP) workflow for the discovery of functional materials. As a test case, quaternary and all-d Heusler compounds were screened for stable compounds with large magnetocrystalline anisotropy energy (
Enda Xiao, Terumasa Tadano
doaj   +1 more source

Parkinson Disease Subtype Stability and Eventual Convergence

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Previous research identified distinct Parkinson disease (PD) clinical subtypes of “Motor Only,” “Psychiatric & Motor,” and “Cognitive & Motor” based on comprehensive, multidomain classification. Whether these subtypes reflect distinct trajectories or progressive disease stages remains unknown.
Laura M. Rosok, Christina N. Lessov‐Schlaggar, Abdulmunaim M. Eid, Erin R. Foster, Baijayanta Maiti, Hailee Domash, Helen Hwang, Richard J. Perrin, Paul T. Kotzbauer, Joel S. Perlmutter, Meghan C. Campbell   +10 more
wiley   +1 more source

Decoding the Stability of Transition-Metal Alloys with Theory-infused Deep Learning

We introduce an interpretable deep learning framework that predicts the cohesive energy of transition-metal alloys (TMAs) by embedding cohesion theory within graph neural networks (GNNs). Beyond accurate prediction of cohesive energy, a key indicator of thermodynamic stability, the model offers mechanistic insights by disentangling energy contributions
Huang, Yang, Wang, Shih-Han, Cao, Shuyi, Achenie, Luke E. K., Xin, Hongliang   +4 more
openaire   +2 more sources

Dynasting Theory: Lessons in learning grounded theory

, 2011
This article captures the key learning lessons gleaned from the author’s experience learning and developing a grounded theory for his doctoral dissertation using the classic methodology as conceived by Barney Glaser. The theory was developed through data
Johnben Teik-Cheok Loy, MBA, MTS, Ph.D.
core  

Patients improve, patterns persist: longitudinal stability of RA joint involvement patterns

Arthritis Care &Research, Accepted Article.
Objective Rheumatoid arthritis is a heterogeneous disease. Data‐driven approaches, from synovial histology to joint involvement patterns (JIPs), have sought to define clinically meaningful subgroups. Whether these subgroups represent stable phenotypes or transient disease states remains unclear.
Tjardo Maarseveen, Yasuo Nagafuchi, Kristina Lend, Seyam Alemyar, Herman Kasper Glas, Gerdur Gröndal, Bjorn Gudbjornsson, Espen A. Haavardsholm, Kim Hørslev Petersen, Jon Lampa, Merete Lund Hetland, Dan Nordström, Mike Nurmohamed, Mikkel Østergaard, Anna Rudin, Marte Schrumpf Heiberg, Tuulikki Sokka‐Isler, Till Uhlig, Josien Veris‐van Dieren, Ronald van Vollenhoven, Tom Huizinga, Marcel JT Reinders, Sytske Anne Bergstra, Erik van den Akker, Rachel Knevel   +24 more
wiley   +1 more source

Does Central Bank Transparency Matter for Economic Stability [PDF]

This paper studies the impact of monetary policy transparency on economic stability, when economic agents are boundedly rational. I first consider a simple class of microfunded general equilibrium models with nominal rigidities and learning.
Stefano Eusepi
core  

On the Importance of Learning Non‐Local Dynamics for Stable Data‐Driven Climate Modeling: A 1D Gravity Wave‐QBO Testbed

Geophysical Research Letters
Model instability remains a core challenge for data‐driven parameterizations, especially those developed with supervised algorithms, and rigorous methods to address it are lacking.
Hamid A. Pahlavan, Pedram Hassanzadeh, M. Joan Alexander   +2 more
doaj   +1 more source

A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems

International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam, Anthony Siming Chen, Guido Herrmann   +2 more
wiley   +1 more source